CN113495281A

CN113495281A - Real-time positioning method and device for movable platform

Info

Publication number: CN113495281A
Application number: CN202110687064.3A
Authority: CN
Inventors: 李昱辰; 钱炜; 杨政; 何晓飞
Original assignee: Hangzhou Fabu Technology Co Ltd
Current assignee: Hangzhou Fabu Technology Co Ltd
Priority date: 2021-06-21
Filing date: 2021-06-21
Publication date: 2021-10-12
Anticipated expiration: 2041-06-21
Also published as: CN113495281B

Abstract

The application provides a real-time positioning method and a real-time positioning device for a movable platform, wherein the movable platform is provided with laser radar equipment, positioning equipment and an inertia measurement unit, and the method comprises the following steps: acquiring point cloud data acquired by laser radar equipment, position data of a movable platform acquired by positioning equipment and driving data acquired by an inertial measurement unit; obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data; and calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose. Compared with the prior art, the method and the device have the advantages that the pose of the movable platform is calibrated in real time through the point cloud data acquired by the laser radar device, the position data of the acquisition platform of the positioning device and the driving data acquired by the inertial measurement unit, so that a high-precision real-time positioning result is obtained, and the robustness of the positioning method is improved.

Description

Real-time positioning method and device for movable platform

Technical Field

The present application relates to the field of real-time positioning technologies, and in particular, to a real-time positioning method and apparatus for a movable platform.

Background

The real-time acquisition of the position of the vehicle in the world coordinate system by utilizing the sensor information in the automatic driving is the basis for realizing other functions in the unmanned driving, and in the unmanned driving system, the laser radar is an indispensable sensor, and because of the high reliability of the data, the realization of high-precision real-time positioning by utilizing the data acquired by the laser radar is a positioning solution commonly used for the existing unmanned driving positioning.

In the existing scheme, a point cloud high-precision map drawn in advance and a point cloud obtained on line are used for matching to obtain real-time positioning.

However, the existing solutions have the following problems: when the off-line map is established and the existing environment is changed greatly, such as surrounding parking vehicles, road construction, surrounding building construction and the like, the information of the off-line map cannot truly reflect the existing environment information, and if the off-line map is directly matched by point cloud, staggered positioning information is brought; according to the matching realization principle, the point cloud on the dynamic object is a point introducing online-offline matching errors, and the problem that when the number of dynamic vehicles around the unmanned vehicle is too large, the number of points which can be used for matching in the point cloud is too small, and the positioning module is often failed is solved. Therefore, the existing solution has the problem of poor robustness of the positioning method of the automatic driving vehicle.

Disclosure of Invention

The embodiment of the application provides a real-time positioning method and a real-time positioning device for a movable platform, and aims to solve the problem that in the prior art, the robustness of a positioning method for an automatic driving vehicle is poor.

A first aspect of the present application provides a real-time positioning method for a movable platform, where the movable platform is equipped with a laser radar device, a positioning device, and an inertial measurement unit, the method including:

acquiring point cloud data acquired by the laser radar equipment, position data of the movable platform acquired by the positioning equipment and driving data acquired by the inertial measurement unit;

obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data;

and calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain a calibrated local pose.

In an optional embodiment, obtaining the estimated absolute pose, the actual local pose, and the actual absolute pose of the movable platform according to the point cloud data, the position data, and the travel data specifically includes:

processing the running data by using a dead reckoning method to obtain an estimated absolute pose of the movable platform;

and processing the point cloud data, the estimated absolute pose and the position data to obtain an actual local pose and an actual absolute pose of the movable platform.

In an optional implementation, the processing the point cloud data, the estimated absolute pose, and the position data to obtain an actual local pose of the movable platform specifically includes:

rasterizing the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of the area around the movable platform;

obtaining off-line point cloud raster map data of the area around the movable platform according to the position data and the off-line map data;

and acquiring the actual local pose of the movable platform according to the online point cloud raster map data and the offline point cloud raster map data.

In an optional implementation manner, the processing the point cloud data and the position data to obtain an actual absolute pose of the movable platform specifically includes:

determining a gray scale map of the area around the movable platform according to the position data and the gray scale map of the whole area;

and obtaining the actual absolute pose of the movable platform according to the gray map of the area around the movable platform and the point cloud data.

In an alternative embodiment, the running data includes an angular velocity and an acceleration, and the estimated absolute pose of the movable platform is obtained by processing the running data using a dead reckoning method, which includes:

for each moment from the acquisition starting moment to the current moment, carrying out integral processing on the angular velocity of each moment, the acceleration of each moment and the noise data of the previous moment to obtain pre-integral data of each moment;

processing pre-integration data from the acquisition starting moment to each moment to obtain an integral value of the current moment;

and obtaining the estimated absolute pose of the movable platform according to the pre-integrated value of the current moment and the gravity direction.

In an optional implementation manner, rasterizing the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of an area around the movable platform, includes:

carrying out distortion removal processing on the point cloud data according to the estimated absolute pose to obtain distortion-free point cloud data;

constructing a plane model based on the road surface according to the distortion-free point cloud data, and obtaining travelable road surface data of the movable platform according to the mass center, the normal vector and the height threshold of the plane model;

removing dynamic point cloud data in the undistorted point cloud data according to the travelable pavement data to obtain static point cloud data;

and rasterizing the static point cloud data according to a preset rule to obtain the online point cloud raster map data.

In an optional implementation manner, the undistorting the point cloud data according to the estimated absolute pose to obtain undistorted point cloud data specifically includes:

predicting the running data of the laser radar equipment in the acquisition time according to the estimated absolute pose, wherein the acquisition time is the time required by the laser radar equipment to rotate for one circle under the preset working frequency;

and removing distorted data caused by self motion in the point cloud data at the acquisition time according to the driving data and a linear interpolation method to obtain the distortion-free point cloud data.

In an alternative embodiment, obtaining offline point cloud grid map data of an area surrounding the movable platform from the location data and offline map data comprises:

rasterizing the off-line map data in the world coordinate system according to the preset rule to obtain the off-line point cloud raster map data;

establishing a corresponding relation between the off-line point cloud raster map data and the off-line point cloud raster map data representation positions according to a Hash formula;

and determining the off-line point cloud raster map data of the area around the movable platform according to the position data and the corresponding relation.

In an alternative embodiment, obtaining the actual absolute pose of the movable platform according to the gray map of the area around the movable platform and the point cloud data includes:

registering the point cloud data at the previous moment and the point cloud data at the current moment to obtain a reflection data registration result and a height data registration result; wherein the point cloud data comprises reflection data and height data;

carrying out weighted average processing on the reflection data registration result and the height data registration result to obtain a height calculation value;

and registering the height calculation value with height information in a gray scale map of the area around the movable platform to obtain the actual absolute pose.

In an optional implementation manner, performing calibration processing on the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain a calibrated local pose includes:

constructing a pose graph of the movable platform according to the estimated absolute pose, the actual local pose and the actual absolute pose;

and optimizing the pose graph by the sliding window optimization method to obtain the calibrated local pose.

A second aspect of the present application provides a real-time positioning device of a movable platform, on which a laser radar apparatus, a positioning apparatus, and an inertial measurement unit are installed, the device including:

the acquisition module is used for acquiring point cloud data acquired by the laser radar equipment, position data of the movable platform acquired by the positioning equipment and driving data acquired by the inertial measurement unit;

a processing module for obtaining an estimated absolute pose, an actual local pose, and an actual absolute pose of the movable platform from the point cloud data, the position data, and the travel data;

and the calibration module is used for calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain a calibrated local pose.

In an alternative embodiment, the processing module is specifically configured to process the travel data using a dead reckoning method to obtain an estimated absolute pose of the movable platform; and processing the point cloud data, the estimated absolute pose and the position data to obtain an actual local pose and an actual absolute pose of the movable platform.

In an optional implementation manner, the processing module is specifically configured to perform rasterization processing on the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of an area around the movable platform; obtaining off-line point cloud raster map data of the area around the movable platform according to the position data and the off-line map data; and acquiring the actual local pose of the movable platform according to the online point cloud raster map data and the offline point cloud raster map data.

In an optional implementation manner, the processing module is specifically configured to determine a gray scale map of an area around the movable platform according to the position data and a gray scale map of a whole area; and obtaining the actual absolute pose of the movable platform according to the gray map of the area around the movable platform and the point cloud data.

In an optional implementation manner, the driving data includes an angular velocity and an acceleration, and the processing module is specifically configured to, for each time from the acquisition start time to the current time, perform integration processing on the angular velocity at each time, the acceleration at each time, and noise data at a previous time to obtain pre-integration data at each time; processing pre-integration data from the acquisition starting moment to each moment to obtain an integral value of the current moment; and obtaining the estimated absolute pose of the movable platform according to the pre-integrated value of the current moment and the gravity direction.

In an optional implementation manner, the processing module is specifically configured to perform distortion removal processing on the point cloud data according to the estimated absolute pose to obtain distortion-free point cloud data; constructing a plane model based on the road surface according to the distortion-free point cloud data, and obtaining travelable road surface data of the movable platform according to the mass center, the normal vector and the height threshold of the plane model; removing dynamic point cloud data in the undistorted point cloud data according to the travelable pavement data to obtain static point cloud data; and rasterizing the static point cloud data according to a preset rule to obtain the online point cloud raster map data.

In an optional embodiment, the processing module is specifically configured to predict, according to the estimated absolute pose, driving data of the lidar device within a collection time, where the collection time is a time required for the lidar device to rotate for one week at a preset operating frequency; and removing distorted data caused by self motion in the point cloud data at the acquisition time according to the driving data and a linear interpolation method to obtain the distortion-free point cloud data.

In an optional implementation manner, the processing module is specifically configured to perform rasterization on offline map data in a world coordinate system according to the preset rule to obtain offline point cloud raster map data; establishing a corresponding relation between the off-line point cloud raster map data and the off-line point cloud raster map data representation positions according to a Hash formula; and determining the off-line point cloud raster map data of the area around the movable platform according to the position data and the corresponding relation.

In an optional implementation manner, the processing module is specifically configured to perform registration on point cloud data at a previous time and point cloud data at a current time to obtain a reflection data registration result and a height data registration result; wherein the point cloud data comprises reflection data and height data; carrying out weighted average processing on the reflection data registration result and the height data registration result to obtain a height calculation value; and registering the height calculation value with height information in a gray scale map of the area around the movable platform to obtain the actual absolute pose.

In an alternative embodiment, the calibration module is configured to construct a pose map of the movable platform according to the estimated absolute pose, the actual local pose, and the actual absolute pose; and optimizing the pose graph by the sliding window optimization method to obtain the calibrated local pose.

A third aspect of the present application provides an electronic device comprising: a processor and a memory;

the memory is used for storing a computer program;

the processor is configured to invoke and execute the computer program stored in the memory to perform the method according to the first aspect.

A fourth aspect of the present application provides a computer-readable storage medium for storing a computer program for causing a computer to perform the method according to the first aspect.

A fifth aspect of the application provides a computer program product comprising a computer program which, when executed by a processor, performs the method according to the first aspect.

The application provides a real-time positioning method and a real-time positioning device for a movable platform, wherein the movable platform is provided with laser radar equipment, positioning equipment and an inertia measurement unit, and the method comprises the following steps: acquiring point cloud data acquired by laser radar equipment, position data of a movable platform acquired by positioning equipment and driving data acquired by an inertial measurement unit; obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data; and calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose. Compared with the prior art, the method removes the distortion caused by the movement of the laser radar equipment and the point cloud data of the dynamic object in the point cloud data through the point cloud data acquired by the laser radar equipment and the driving data acquired by the inertial measurement unit so as to obtain the accurate estimation absolute pose, and the off-line map obtained according to the position data is maintained according to the point cloud data, so that the dependence of a positioning technology on the off-line map is overcome, and when the environment of the movable platform changes, an accurate positioning result can be obtained, and the actual absolute pose of the movable platform in the two-dimensional map is obtained according to the point cloud data, and then the calibration is carried out according to the estimated absolute pose, the actual local pose and the actual absolute pose, the continuity and the smoothness of the local pose obtained in different environments are ensured, and the robustness of the positioning method is further improved.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the following briefly introduces the drawings needed to be used in the description of the embodiments or the prior art, and obviously, the drawings in the following description are some embodiments of the present invention, and those skilled in the art can obtain other drawings according to the drawings without inventive labor.

Fig. 1 is a schematic application scenario diagram of a real-time positioning method for a movable platform according to an embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of a movable platform according to an embodiment of the present disclosure;

fig. 3 is a schematic flowchart of a real-time positioning method for a movable platform according to an embodiment of the present disclosure;

fig. 4 is a schematic flowchart of another real-time positioning method for a movable platform according to an embodiment of the present disclosure;

fig. 5 is a schematic flowchart of a real-time positioning method for a movable platform according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a real-time positioning apparatus for a movable platform according to an embodiment of the present disclosure;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The real-time acquisition of the position of the vehicle in the world coordinate system by utilizing the sensor information in the automatic driving is the basis for realizing other functions in the unmanned driving, and in the unmanned driving system, the laser radar is an indispensable sensor, and because of the high reliability of the data, the realization of high-precision real-time positioning by utilizing the data acquired by the laser radar is a positioning solution commonly used for the existing unmanned driving positioning. In the existing scheme, a point cloud high-precision map drawn in advance and a point cloud obtained on line are used for matching to obtain real-time positioning.

In order to solve the problems, the application provides a real-time positioning method and a real-time positioning device for a movable platform, wherein laser radar equipment, positioning equipment and an inertia measurement unit are installed on the movable platform, off-line data used for positioning and determined according to position data collected by the positioning equipment is optimized through point cloud data collected by the laser radar equipment, positioning data is calibrated according to an optimization result, and a positioning result is obtained.

The following explains an application scenario of the present application.

Fig. 1 is a schematic application scenario diagram of a real-time positioning method for a movable platform according to an embodiment of the present application. As shown in fig. 1, the pose data of the movable platform 001 is determined by the surrounding environment information of the movable platform 001 and removing the influence of the self-motion information and the surrounding dynamic information, so that the movable platform 001 is not influenced by the environment and is positioned in real time. Fig. 2 is a schematic structural diagram of a movable platform according to an embodiment of the present disclosure, and as shown in fig. 2, a movable platform 001 includes: the laser radar device 002, the positioning device 003 and the inertial measurement unit 004 optimize and calibrate the pose data of the movable platform 001 through the laser radar device 002, the positioning device 003 and the inertial measurement unit 004 to obtain accurate real-time pose data, so that the robustness of the positioning technology is improved.

In the embodiment of the present application, the apparatus for implementing the real-time positioning function of the movable platform may be the movable platform, or may be an apparatus capable of supporting implementing the function, such as a system on chip, and the apparatus may be installed in the movable platform. In the embodiment of the present application, the chip system may be composed of a chip, and may also include a chip and other discrete devices.

It should be noted that the application scenario of the technical solution of the present application may be the scenario in fig. 1, but is not limited to this, and may also be applied to other scenarios requiring real-time positioning of the movable platform.

It can be understood that the above-mentioned real-time positioning method for the movable platform can be implemented by the real-time positioning apparatus for the movable platform provided in the embodiments of the present application, and the real-time positioning apparatus for the movable platform may be part or all of a certain device, for example, a chip on the movable platform.

The following takes a real-time positioning device integrated or installed with a movable platform of relevant execution codes as an example, and the technical solution of the embodiment of the present application is described in detail with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.

Fig. 3 is a schematic flow chart of a real-time positioning method for a movable platform according to an embodiment of the present application, where an execution main body of the embodiment is the movable platform, and relates to a specific process of real-time positioning of the movable platform. As shown in fig. 3, the method includes:

s101, point cloud data collected by laser radar equipment, position data of a movable platform collected by positioning equipment and driving data collected by an inertia measurement unit are obtained.

The point cloud data refers to a set of vectors in a three-dimensional coordinate system. The data collected by the lidar device are recorded in the form of points, each point containing three-dimensional coordinates, possibly colour data, height data or reflection data.

The type of the positioning device in the embodiment of the present application is not limited, and may be, for example, a Real Time Kinematic (RTK) measuring device.

In the embodiment of the present application, an Inertial Measurement Unit (IMU) is used to collect travel data of the movable platform.

The travel data includes, among other things, angular velocity and acceleration.

And S102, obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data.

The estimated absolute pose is current pose data of the movable platform obtained according to the driving data acquired by the IMU. And the actual local pose is obtained by rasterizing the point cloud data, and then matching and updating the point cloud data with an offline raster map determined by the position data to obtain the pose data of the movable platform in the laser sensor. The actual absolute pose is pose data obtained by comparing the height data and the reflection data in the point cloud data with the two-dimensional gray map of the position data and updating the gray data in the gray map.

In the embodiment of the application, the distorted point cloud data caused by the movement of the laser radar equipment in the point cloud data is removed through the driving data, and the point cloud information of the surrounding dynamic objects is removed according to the point cloud data, so that the error in the point cloud data is removed, and the robustness of the positioning method is improved.

S103, calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose.

And constructing a pose graph of the movable platform according to the estimated absolute pose, the actual absolute pose and the actual local pose, and calibrating the pose graph according to a sliding window optimization method to obtain the calibrated local pose.

And when marginalization is carried out in the sliding window optimization method, the constraint relation between the pose data is kept through Jacobian coefficients (Jacobian), so that the obtained pose data cannot be mutated, and the continuity and smoothness of a real-time positioning result are ensured.

The embodiment of the application provides a real-time positioning method of a movable platform, wherein laser radar equipment, positioning equipment and an inertia measurement unit are installed on the movable platform, and the method comprises the following steps: acquiring point cloud data acquired by laser radar equipment, position data of a movable platform acquired by positioning equipment and driving data acquired by an inertial measurement unit; obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data; and calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose. Compared with the prior art, the method removes the distortion caused by the movement of the laser radar equipment and the point cloud data of the dynamic object in the point cloud data through the point cloud data acquired by the laser radar equipment and the driving data acquired by the inertial measurement unit so as to obtain the accurate estimation absolute pose, and the off-line map obtained according to the position data is maintained according to the point cloud data, so that the dependence of a positioning technology on the off-line map is overcome, and when the environment of the movable platform changes, an accurate positioning result can be obtained, and the actual absolute pose of the movable platform in the two-dimensional map is obtained according to the point cloud data, and then the calibration is carried out according to the estimated absolute pose, the actual local pose and the actual absolute pose, the continuity and the smoothness of the local pose obtained in different environments are ensured, and the robustness of the positioning method is further improved.

On the basis of the above embodiments, the following further describes the real-time positioning method of the movable platform of the first device of the dual system provided in the present application. Fig. 4 is a schematic flowchart of another real-time positioning method for a movable platform according to an embodiment of the present application, where as shown in fig. 4, the method includes:

s201, point cloud data collected by laser radar equipment, position data of a movable platform collected by positioning equipment and driving data collected by an inertia measurement unit are obtained.

The technical terms, technical effects, technical features, and optional embodiments of S201 can be understood with reference to S101 shown in fig. 3, and repeated descriptions will not be repeated here.

S202, obtaining an estimated absolute pose, an actual local pose and an actual absolute pose of the movable platform according to the point cloud data, the position data and the driving data.

Optionally, a dead reckoning method is used for processing the running data to obtain an estimated absolute pose of the movable platform; and processing the point cloud data, the estimated absolute pose and the position data to obtain the actual local pose and the actual absolute pose of the movable platform.

In the embodiment of the application, the mode of processing the running data by using a dead reckoning method to obtain the estimated absolute pose of the movable platform is not limited, and for each time from the acquisition starting time to the current time, for example, the angular velocity of each time, the acceleration of each time and the noise data of the previous time are subjected to integral processing to obtain pre-integral data of each time; processing pre-integration data from the acquisition starting moment to each moment to obtain an integral value of the current moment; and obtaining the estimated absolute pose of the movable platform according to the pre-integral value and the gravity direction at the current moment.

The dead reckoning method is a method for calculating the position of the next time by measuring the moving distance and direction under the condition of the position of the current time.

In the embodiment of the application, the noise data comprises noise data of an accelerometer and noise data of a gyroscope.

In the embodiment of the application, the pre-integration data is only related to the integration time period and the time length from the starting time to the current time.

And integrating the pre-integration data to obtain an integral value, and obtaining an estimated absolute pose according to the gravity direction at the current moment. And estimating the absolute pose as pose data of the movable platform at the next moment according to the running data at the current moment. The position and orientation data packet includes position data, speed data, orientation data and the like.

In the embodiment of the present application, processing point cloud data, estimated absolute pose, and position data to obtain an actual local pose and an actual absolute pose of a movable platform includes: rasterizing the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of the area around the movable platform; obtaining off-line point cloud raster map data of the area around the movable platform according to the position data and the off-line map data; and obtaining the actual local pose of the movable platform according to the online point cloud raster map data and the offline point cloud raster map data.

Specifically, rasterization processing is performed on the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of the area around the movable platform, and the method comprises the following steps: carrying out distortion removal processing on the point cloud data according to the estimated absolute pose to obtain distortion-free point cloud data; constructing a plane model based on the road surface according to the distortion-free point cloud data, and obtaining travelable road surface data of the movable platform according to the mass center, the normal vector and the height threshold of the plane model; removing dynamic point cloud data in the undistorted point cloud data according to the travelable road surface data to obtain static point cloud data; and rasterizing the static point cloud data according to a preset rule to obtain online point cloud raster map data.

The method comprises the following steps of carrying out distortion removal processing on point cloud data according to an estimated absolute pose to obtain distortion-free point cloud data, wherein the distortion removal processing comprises the following steps: predicting the driving data of the laser radar equipment in the acquisition time according to the estimated absolute pose, wherein the acquisition time is the time required by the laser radar equipment to rotate for one circle under the preset working frequency; and removing distorted data of the laser radar equipment in the point cloud data due to self movement in the acquisition time according to the driving data and a linear interpolation method to obtain distortion-free point cloud data.

The setting of the preset operating frequency is not limited in the embodiment of the present application, and may be, for example, 10 hz.

The linear interpolation method is to construct a linear motion curve of the laser radar according to the driving data and interpolate the linear motion curve.

In the embodiment of the application, a plane model based on the road surface is constructed according to the distortion-free point cloud data, and the travelable road surface data of the movable platform are obtained according to the mass center, the normal vector and the height threshold of the plane model; and removing the dynamic point cloud data in the undistorted point cloud data according to the travelable road surface data to obtain static point cloud data.

Specifically, a plane model based on the road surface is constructed according to the distortion-free point cloud data, the mass center and the normal vector of the plane model are obtained through the point cloud data, and the travelable road surface data of the movable platform are obtained according to the height threshold.

The driving-capable road surface data form road surface data capable of being driven by the movable platform and data of guardrails, trees and the like outside the road surface capable of being driven.

And whether each point cloud data is the dynamic point cloud data of the dynamic object in the driving road surface data, such as other vehicles, pedestrians and the like, can be judged according to the height threshold, so that the influence of the dynamic point cloud data is removed, and the positioning precision is improved.

In the embodiment of the present application, the setting of the height threshold is not limited, and may be described according to specific situations, and if the movable platform is an autonomous vehicle, the height threshold may be set by counting the height information of the vehicle on the market.

The preset rule is not limited in the embodiment of the present application, and for example, a grid of 50cm by 50cm may be set.

Further, in the embodiment of the present application, obtaining the offline point cloud grid map data of the area around the movable platform according to the position data and the offline map data includes: rasterizing the offline map data in the world coordinate system according to a preset rule to obtain offline point cloud raster map data; establishing a corresponding relation between the off-line point cloud raster map data and the off-line point cloud raster map data representation positions according to a Hash formula; and determining the off-line point cloud raster map data of the area around the movable platform according to the position data and the corresponding relation.

According to the embodiment of the application, after the actual local pose of the movable platform is obtained according to the online point cloud raster map data and the offline point cloud raster map data, the offline point cloud raster map data is updated through the online point cloud raster map data.

Further, in this embodiment of the present application, processing the point cloud data and the position data to obtain an actual absolute pose of the movable platform includes: determining a gray scale map of the area around the movable platform according to the position data and the gray scale map of the whole area; and obtaining the actual absolute pose of the movable platform according to the gray map and the point cloud data of the area around the movable platform.

And then, height data and reflection data in the static point cloud data are obtained.

It is known that the height data and the reflection data characterize height information in the point cloud data.

Specifically, the actual absolute pose of the movable platform is obtained according to the gray map and the point cloud data of the area around the movable platform, and the method comprises the following steps: registering the point cloud data at the previous moment and the point cloud data at the current moment to obtain a reflection data registration result and a height data registration result; wherein the point cloud data comprises reflection data and height data; carrying out weighted average processing on the registration result of the reflection data and the registration result of the height data to obtain a height calculation value; and registering the height calculation value with height information in a gray scale map of the area around the movable platform to obtain an actual absolute pose.

And S203, calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose.

Specifically, a pose graph of the movable platform is constructed according to the estimated absolute pose, the actual local pose and the actual absolute pose; and optimizing the pose graph by a sliding window optimization method to obtain the calibrated local pose.

On the basis of the foregoing embodiment, fig. 5 is a schematic flowchart of a real-time positioning method for a movable platform according to an embodiment of the present application, and as shown in fig. 5, the method includes:

s301, point cloud data collected by the laser radar device, position data of the movable platform collected by the positioning device and driving data collected by the inertia measurement unit are obtained.

The following describes a case where the inertia measurement unit collects travel data.

In the embodiment of the present application, the angular velocity obtained by the angular velocity meter in the IMU, the acceleration obtained by the accelerometer, and the IMU noise data assuming that the movable platform is stationary in the process of acquiring data by the IMU include: the noise data ba of the accelerometer and the noise data bg of the gyroscope are calculated through pre-integration to obtain the data neededThe pre-integration data of (a) are respectively the position pre-integration amount p and its differential data to the noise data of the accelerometer ba and the noise data of the gyroscope bg:

and

velocity pre-integration data v and its differential quantities to noise data ba of accelerometer and noise data bg of gyroscope

And

angle pre-integration data q and its differential to noise data bg of gyroscope

These pre-integration data are only related to the time period of integration.

The following explains the case where the laser radar apparatus collects point cloud data.

The laser radar equipment works at the working frequency of 10 Hz, the mechanical laser radar equipment rotates for a circle within 0.1s, and the surrounding geometric information is converted into point cloud data through the principle of light reflection.

The following description is made of a case where the positioning apparatus acquires position data of the movable platform.

Position data of the movable platform in the world coordinate system is obtained through RTK.

And S302, processing the running data by using a dead reckoning method to obtain an estimated absolute pose of the movable platform.

Because the operating frequency of the IMU is 100 Hz, the pre-integral data integrated from the first frame is utilized, the time length of the integration and the gravity direction of the pre-integral data are combined, the corresponding estimated absolute pose is generated according to the pre-integral data, and the estimated absolute pose is output to other modules of the movable platform, such as a control module, a sensing module and the like, so as to carry out subsequent work on the movable platform. Wherein estimating the absolute pose may comprise: position data P, velocity data V, rotation data Q.

The first frame corresponds to the starting time, each frame corresponds to one time, and the time sequence is consistent.

And S303, carrying out distortion removal processing on the point cloud data according to the estimated absolute pose to obtain distortion-free point cloud data.

Specifically, because the laser radar equipment moves within 0.1s, point cloud data are not collected at a static point, each point cloud data contains distortion caused by the self-movement of the laser radar equipment, an estimated absolute pose is obtained through the IMU and is used as estimated movement data of the laser radar equipment, the point cloud data obtained by scanning in each period are subjected to linear interpolation to eliminate the distortion data of the movement of the laser radar equipment according to the difference value of the generated time and the scanning starting time, and finally distortion-free point cloud data are obtained.

S304, constructing a plane model based on the road surface according to the distortion-free point cloud data, and obtaining travelable road surface data of the movable platform according to the mass center, the normal vector and the height threshold of the plane model.

Specifically, the point cloud data around the movable platform may be from a dynamic object, the point cloud data is contrary to a basic static assumption, on the basis of the undistorted point cloud data, a plane model based on a road surface is constructed through the undistorted point cloud data, a mass center and a normal vector of the plane model are maintained through the point cloud data, after the point cloud data is input, whether the point is a point in the plane model or not is judged according to a distance from the point in the point cloud data to the surface, and after the plane is obtained, travelable road surface data corresponding to the movable platform can be obtained.

S305, removing dynamic point cloud data in the undistorted point cloud data according to the travelable road surface data to obtain static point cloud data.

All points on the travelable road surface data of the movable platform are possibly dynamic objects, and in order to avoid introducing errors, all the points above the travelable road surface data are taken as dynamic points to be removed to obtain static point cloud data.

And S306, determining the off-line point cloud raster map data of the area around the movable platform according to the position data and the corresponding relation.

In the embodiment of the application, the off-line point cloud grid map data is established to enable the initialization process of the positioning of the movable platform to be more stable, the establishment process mainly comprises the steps of collecting point cloud data and position data of a target area through a movable platform carrying RTK positioning hardware equipment and laser radar equipment, removing dynamic point cloud data obtained in the driving process through a manual removing means, obtaining position data by combining with RTK, obtaining off-line spliced point cloud map data, forming dense point cloud map data of the area, and finally storing the point cloud map data into three-dimensional off-line point cloud grid map data according to the position data. The rasterization processing mode is that a three-dimensional space is divided into grid cubes of 50cm x 50cm, each cube stores related point cloud data, each point cloud corresponds to a corresponding grid according to position data of the point cloud, the stored related point cloud data are position mean values, second moments, feature matrixes, final updating time and confidence coefficients of the point clouds in the grids, and the final updating time and the confidence coefficients are used for later-stage matching optimization. Putting the off-line point cloud map data into each grid according to the position data, calculating a position mean value, a second moment, a characteristic matrix, a final updating time and a confidence coefficient corresponding to the off-line point cloud map data, wherein a variance matrix can be obtained after the mean value and the second moment are obtained, the distribution condition of the grid point cloud can be obtained after the variance matrix is decomposed through the characteristic value, and the off-line point cloud map data can be divided into the following specific values according to the size of the characteristic value: the maximum value Pmax, the middle value Pmid and the minimum value Pmin, when Pmin/Pmax <0.05 and Pmid/Pmax <0.05, the grid can be considered as a straight line feature, when Pmin/Pmid <0.2 and Pmin/Pmax <0.2, the grid can be considered as a plane feature, when all three feature values are <0.05, the grid can be considered as a point feature, and finally, according to the obtained features of the grid, a feature matrix is generated to store corresponding information. In addition, in order to enable the point cloud data of the determined position data to quickly find a proper grid, the method and the device utilize the hash table to quickly search according to the position data to obtain a grid corresponding to the position data, and therefore off-line point cloud grid map data drawn in advance is obtained.

And S307, rasterizing the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data of the area around the movable platform.

Through static point cloud data, the point cloud data are firstly converted into a local coordinate system of the movable platform in the application from a laser radar coordinate system, wherein the point cloud data marked as ground points form large raster plane data to form plane constraint, and other point cloud data can form corresponding point plane constraint and point line constraint according to the characteristics of a raster. In the embodiment of the application, the pose data of the frame of point cloud data in the local coordinate system is used as the state quantity, and an optimized actual local pose is finally obtained according to the formed constraint relation. In addition, according to the uncertain relation of the point cloud data, sampling is carried out in the ray direction, and grids with the maximum confidence coefficient are added into optimization.

And S308, obtaining the actual local pose of the movable platform according to the online point cloud raster map data and the offline point cloud raster map data.

Converting the point cloud data into a local coordinate system, adding the point cloud data into a corresponding grid according to the rasterization processing method in the step S306, and updating the information stored in the grid, wherein the updating modes of the position mean, the second moment and the feature matrix are consistent with those in the step S306, and the mode of updating the confidence coefficient is as follows: and dividing the characteristic relation corresponding to the grids into point-surface distance and point-line distance, and then converting the distance values into Gaussian distribution with the mean value of 0 and the variance of 2 to obtain corresponding probability values which are accumulated to the confidence coefficient of the corresponding grids. And after traversing all the point clouds, obtaining an updated point cloud grid map and an actual local pose.

And S309, obtaining the actual absolute pose of the movable platform according to the gray map and the point cloud data of the area around the movable platform.

In the embodiment of the application, an RTK and a laser radar device are used to draw a target area in advance, wherein the RTK provides accurate position data of world coordinates at any time, and the laser radar device provides scene data of a surrounding scene at corresponding time, wherein the scene data includes: reflection data and height data. In the embodiment of the application, the point cloud data represents reflection data and height data of a corresponding road section or area, two matching results can be generated when the two data are matched on line, and then the two results are fused to optimize the final positioning result. And constructing reliable point cloud reflection map data and point cloud height map data by using the two data and assisting various rear-end optimization schemes.

In the embodiment of the application, the registration of the point cloud reflection map data and the point cloud height map data is mainly divided into two parts. The first part is that the reflection data obtained by real-time scanning of the laser radar equipment is registered with the point cloud reflection map data constructed previously, and scene surfaces of different materials are presented by different reflectivity values in the scanning result of the laser radar equipment, so that the reflectivity can objectively and comprehensively express the material composition of the scene surfaces, and the method is also an important positioning basis. In the actual operation process, firstly, a real-time scanning result is obtained through laser radar equipment, a reflectivity value is extracted, a corresponding position is found on point cloud reflection map data with the reflectivity value according to a real-time positioning result of a moment on a movable platform at that time, a search area is set, and a position with the highest matching degree with the reflectivity value in the current scanning result is searched in the search area to serve as a registration result; in addition, the second part registers height data obtained by real-time scanning of the laser radar equipment with previously constructed point cloud height map data, and the principle is that the point cloud data in a laser scanning result returned by the laser radar equipment has three-dimensional position information relative to a laser emission point, so that the height data of each laser point can be obtained, and the point cloud height map data can objectively and comprehensively express the height data of the surface in a scene. Theoretically, the registration result of the second part should have higher robustness than that of the first part, mainly because the height data of the surrounding object is substantially unchanged, but the reflection data of the laser point cloud is related to various factors such as the working state of the laser, the humidity of the surrounding environment, the distance of the reflection object and the like, the robustness is relatively insufficient, but the matching result still has certain referential property. Therefore, a reasonable fusion mode is required for obtaining a reasonable output result for the two matching results.

Two registration results are obtained, and in the actual operation process, it is found that although the two registration results should be completely consistent theoretically, in the actual situation, because of the change of scene information and the like, the two registration results are often inconsistent, and a method needs to be found to fuse the two registration results. In the embodiment of the application, the registration result with higher matching degree with the corresponding point cloud data is more credible, so that an artificial bias term a is added to be 1.2 on the basis of the self-adaptive fusion result based on the covariance. Wherein, the covariance item of the matching process is obtained in the matching work of each part, wherein, the covariance item of the height data matching result is m, the covariance item of the reflection data is n, according to the conclusion, the fusion weight of the matching result of the final point cloud height map data is m according to the self-adaptive fusion scheme and artificial bias

The fusion weight of the matching result of the point cloud reflection map data is

And S310, calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain the calibrated local pose.

In the embodiment of the present application, when calibration is performed, when the overall state is not initialized, particularly, the state quantity speed data V, IMU noise and the like are not started with a value of zero, the corresponding values thereof may be aligned in a linear or nonlinear manner, and initialization processing is performed according to the obtained pre-integration data of the IMU and pose sequencing in a time series. Firstly, converting the direction of gravity to the lower part of a local coordinate system in the embodiment of the application, firstly initializing bg related to angular velocity in IMU noise, supposing that the bg is kept unchanged in local time, forming a nonlinear optimization problem by the hypothesis through mathematical expression, wherein the bg obtained through optimization is a nonlinear alignment result; after an optimized result bg is obtained, interframe position variation and speed variation under a fixed coordinate system can be constructed through the estimated gravity direction, a linear equation is constructed through the construction of the formula, the alignment quantity of the speed can be obtained through the linear solution of the matrix, and the process is a linear rough alignment process of the speed; and then according to the obtained result, constructing inter-frame speed term constraint and position term constraint in the pre-integration constraint by taking the speed data V and the noise term ba of the IMU as state quantities, and finally constructing a nonlinear optimization problem and solving to obtain a nonlinear aligned V and ba.

According to the initialization result, the state quantity initialization quantity is obtained, and finally, a sliding optimization window which is mainly in the form of pose mutual constraint and is fixed in frame number is constructed according to the previous result in the embodiment of the application. The window comprises a frame of point cloud data corresponding to one frame of unit in the window, and state quantities contained in the frame of data comprise position data P, speed data V, rotation data Q, IMU noise data ba and bg, external parameters of laser radar equipment and an IMU (inertial measurement Unit) directly, including a position external parameter Ex.p and an orientation external parameter Ex.q, and are marginalized quantities for realizing window sliding and prior quantities reserved for global matching results. In the embodiment of the present application, the number of windows remains fixed after the operation, but a range of 10 frames to 100 frames may be set initially according to the requirement. And after the state quantity is determined, carrying out constraint construction according to results obtained by the modules, and finally constructing a complete optimization problem. The IMU pre-integration number can form an interframe pre-integration constraint in an optimization window, and the state quantities of the IMU pre-integration number include position data P, speed data V, rotation data Q, IMU, noise data ba and bg; in addition, each frame has an obtained actual local pose, mutual pose graph constraint between frames can be formed, the state quantities of the mutual pose graph constraint are position data P, speed data V and rotation data Q, and the obtained actual local pose provides a corresponding state optimization initial value and observation information; in addition, the actual absolute pose is converted to the lower side of the local coordinate system fixed by the application to form prior constraint of the frame, the corresponding state quantities are position data P and speed data V, and the actual local pose converted by the registration module is observed; finally, since the number of optimization windows is constant, when the number of frames participating in the optimization is greater than the limit number, the discussion will be divided into two cases: the first case is that the module of the position difference between the last but one window frame and the last but one window frame is less than 5m and the time difference between the two frames is less than 0.1s, the case adds the last but one window frame directly, the corresponding data is discarded directly, in the other case, the data of the frame entering the window with the earliest time is marginalized and marginalized to the marginalization quantity of the frame behind it, because the parameters of the frame sliding out of the window are not optimized after the sliding window is introduced, but the frame and the data in the sliding window still have the constraint, the directly discarding the frame out of the window can cause the loss of the constraint information, so the frame is packaged into the prior information and added to the large nonlinear optimization problem as a part of errors, the step is marginalization; therefore, the preserving the linearization status of a frame when the preserving frame is marginalized includes: and finally, forming marginalization constraint through the change caused by the change of the linearization point in subsequent optimization. Finally, according to the result of pose graph optimization, an optimized estimated value of local pose and IMU noise data can be obtained in the embodiment of the application.

And in addition, estimation of IMU noise data is used for updating a state integral recursion result, and the local pose is used for real-time positioning of the movable platform.

Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

Fig. 6 is a schematic structural diagram of the real-time positioning device for a movable platform provided in the embodiment of the present application, and the real-time positioning device for a movable platform may be implemented by software, hardware, or a combination of the software and the hardware to execute the real-time positioning method for a movable platform in the above embodiments. As shown in fig. 6, the laser radar device, the positioning device and the inertial measurement unit are mounted on the movable platform, and the real-time positioning apparatus 400 of the movable platform includes: an acquisition module 401, a processing module 402 and a calibration module 403.

The acquisition module 401 is configured to acquire point cloud data acquired by a laser radar device, position data of a movable platform acquired by a positioning device, and driving data acquired by an inertial measurement unit;

a processing module 402, configured to obtain an estimated absolute pose, an actual local pose, and an actual absolute pose of the movable platform according to the point cloud data, the position data, and the driving data;

and a calibration module 403, configured to perform calibration processing on the actual local pose according to the estimated absolute pose and the actual absolute pose, and obtain a calibrated local pose.

In an alternative embodiment, the processing module 402 is specifically configured to process the driving data by using a dead reckoning method to obtain an estimated absolute pose of the movable platform; and processing the point cloud data, the estimated absolute pose and the position data to obtain the actual local pose and the actual absolute pose of the movable platform.

In an optional implementation manner, the processing module 402 is specifically configured to perform rasterization processing on the point cloud data according to the estimated absolute pose, so as to obtain online point cloud raster map data of an area around the movable platform; obtaining off-line point cloud raster map data of the area around the movable platform according to the position data and the off-line map data; and obtaining the actual local pose of the movable platform according to the online point cloud raster map data and the offline point cloud raster map data.

In an alternative embodiment, the processing module 402 is specifically configured to determine a grayscale map of an area around the movable platform according to the position data and the grayscale map of the whole area; and obtaining the actual absolute pose of the movable platform according to the gray map and the point cloud data of the area around the movable platform.

In an optional implementation manner, the driving data includes an angular velocity and an acceleration, and the processing module is specifically configured to perform, for each time from the acquisition start time to the current time, integration processing on the angular velocity at each time, the acceleration at each time, and noise data at a previous time to obtain pre-integration data at each time; processing pre-integration data from the acquisition starting moment to each moment to obtain an integral value of the current moment; and obtaining the estimated absolute pose of the movable platform according to the pre-integral value and the gravity direction at the current moment.

In an optional implementation manner, the processing module 402 is specifically configured to perform distortion removal processing on the point cloud data according to the estimated absolute pose to obtain distortion-free point cloud data; constructing a plane model based on the road surface according to the distortion-free point cloud data, and obtaining travelable road surface data of the movable platform according to the mass center, the normal vector and the height threshold of the plane model; removing dynamic point cloud data in the undistorted point cloud data according to the travelable road surface data to obtain static point cloud data; and rasterizing the static point cloud data according to a preset rule to obtain online point cloud raster map data.

In an alternative embodiment, the processing module 402 is specifically configured to predict, according to the estimated absolute pose, the driving data of the lidar device within the acquisition time, where the acquisition time is a time required for the lidar device to rotate for one circle at a preset operating frequency; and removing distorted data caused by self motion in the point cloud data at the acquisition time according to the driving data and a linear interpolation method to obtain distortion-free point cloud data.

In an optional implementation manner, the processing module 402 is specifically configured to perform rasterization on offline map data in a world coordinate system according to a preset rule to obtain offline point cloud raster map data; establishing a corresponding relation between the off-line point cloud raster map data and the off-line point cloud raster map data representation positions according to a Hash formula; and determining the off-line point cloud raster map data of the area around the movable platform according to the position data and the corresponding relation.

In an optional implementation manner, the processing module 402 is specifically configured to perform registration on point cloud data at a previous time and point cloud data at a current time, so as to obtain a reflection data registration result and a height data registration result; wherein the point cloud data comprises reflection data and height data; carrying out weighted average processing on the registration result of the reflection data and the registration result of the height data to obtain a height calculation value; and registering the height calculation value with height information in a gray scale map of the area around the movable platform to obtain an actual absolute pose.

In an alternative embodiment, the calibration module 403 is configured to construct a pose graph of the movable platform according to the estimated absolute pose, the actual local pose, and the actual absolute pose; and optimizing the pose graph by a sliding window optimization method to obtain the calibrated local pose.

It should be noted that the real-time positioning device for a movable platform provided in the embodiments of the present application may be used to execute the method provided in any of the above embodiments, and the specific implementation manner and the technical effect are similar and will not be described herein again.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 7, the first device 500 may include: at least one processor 501 and memory 502. Fig. 7 shows a first device as an example of a processor.

The memory 502 is used for storing programs. In particular, the program may include program code including computer operating instructions.

Memory 502 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 501 is configured to execute computer-executable instructions stored in the memory 502 to implement the above-mentioned real-time positioning method for the movable platform;

the processor 501 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the embodiments of the present Application.

Alternatively, in a specific implementation, if the communication interface, the memory 502 and the processor 501 are implemented independently, the communication interface, the memory 502 and the processor 501 may be connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. Buses may be classified as address buses, data buses, real-time location buses for mobile platforms, etc., but do not represent only one bus or type of bus.

Alternatively, in a specific implementation, if the communication interface, the memory 502 and the processor 501 are integrated into a chip, the communication interface, the memory 502 and the processor 501 may complete communication through an internal interface.

The present application also provides a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, are specifically, the computer-readable storage medium stores program information, and the program information is used for the real-time positioning method of the mobile platform.

Embodiments of the present application also provide a program, which when executed by a processor, is configured to perform the method for real-time positioning of a movable platform provided in the above method embodiments.

Embodiments of the present application further provide a program product, such as a computer-readable storage medium, having instructions stored therein, which when executed on a computer, cause the computer to perform the method for real-time positioning of a movable platform provided in the above method embodiments.

In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the invention are brought about in whole or in part when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time positioning method of a movable platform, wherein a laser radar device, a positioning device and an inertial measurement unit are installed on the movable platform, and the method is characterized by comprising the following steps:

2. The positioning method according to claim 1, wherein obtaining the estimated absolute pose, the actual local pose, and the actual absolute pose of the movable platform from the point cloud data, the position data, and the travel data comprises:

3. The positioning method according to claim 2, wherein processing the point cloud data, the estimated absolute pose, and the position data to obtain an actual local pose of the movable platform comprises:

4. The positioning method according to claim 2, wherein the processing the point cloud data and the position data to obtain an actual absolute pose of the movable platform comprises:

5. The method of claim 2, wherein the travel data comprises angular velocity and acceleration, and wherein the travel data is processed using a dead reckoning method to obtain an estimated absolute pose of the movable platform, in particular comprising:

6. The method of claim 3, wherein rasterizing the point cloud data according to the estimated absolute pose to obtain online point cloud raster map data for an area surrounding the movable platform comprises:

7. The method according to claim 6, wherein the point cloud data is subjected to distortion removal processing according to the estimated absolute pose to obtain distortion-free point cloud data, and the method specifically comprises the following steps:

and removing distortion data of the laser radar equipment caused by self movement in the acquisition time in the point cloud data according to the driving data and a linear interpolation method to obtain the distortion-free point cloud data.

8. The method of any of claims 3-7, wherein obtaining offline point cloud grid map data for an area surrounding the movable platform from the location data and offline map data comprises:

9. The method of claim 4, wherein obtaining the actual absolute pose of the movable platform from the grey-scale map of the area surrounding the movable platform and the point cloud data comprises:

10. The method of claim 1, wherein calibrating the actual local pose according to the estimated absolute pose and the actual absolute pose to obtain a calibrated local pose comprises:

and optimizing the pose graph by a sliding window optimization method to obtain a calibrated local pose.

11. The utility model provides a real-time positioner of portable platform, install laser radar equipment, positioning device and inertial measurement unit on the portable platform which characterized in that, the device includes:

12. An electronic device, comprising: a processor and a memory;

the memory is used for storing a computer program;

the processor is configured to call and execute a computer program stored in the memory, and to perform the method according to any one of claims 1 to 10.

13. A computer-readable storage medium for storing a computer program which causes a computer to perform the method of any one of claims 1-10.

14. A computer program product comprising a computer program, characterized in that the computer program realizes the method according to any of claims 1-10 when executed by a processor.