KR102482048B1 - Method for trajectory correction for 3D map creation, and computer program recorded on record-medium for executing method therefor - Google Patents

Abstract

The present invention proposes a trajectory correction method. The method comprises: a step in which a map generation device corrects errors due to a change in location using 3D point cloud data collected in real time through LiDAR, whose location changes in real time; a step in which the map generation device estimates an expected trajectory (odometry) related to the change in the location based on the corrected 3D point cloud data; a step in which the map generation device applies curve fitting to the estimated expected trajectory; and a step in which the map generation device maps an expected trajectory before the curve fitting to the expected trajectory to which the curve fitting is applied. Through this, the present invention corrects errors in 3D point cloud data collected through LiDAR and generates a 3D map through a mapped trajectory by applying curve fitting to an expected trajectory estimated through the error-corrected 3D point cloud data, thereby minimizing errors occurring in the process of generating the 3D map.

Description

Method for trajectory correction for 3D map creation, and computer program recorded on record-medium for executing method therefor}

The present invention relates to a Location Based Service (LBS). More specifically, it relates to a trajectory correction method capable of minimizing errors that may occur in the process of generating a 3D map based on LiDAR data and a computer program recorded on a recording medium to execute the same.

Autonomous driving of a vehicle refers to a system that can judge and drive a vehicle by itself. Such autonomous driving may be classified into gradual stages from non-automation to complete automation according to the degree of involvement of the system in driving and the degree of control of the vehicle by the driver. In general, the level of autonomous driving is divided into six levels classified by the Society of Automotive Engineers (SAE) International. According to the six levels classified by the International Association of Automotive Engineers, level 0 is non-automation, level 1 is driver assistance, level 2 is partial automation, level 3 is conditional automation, level 4 is highly automated, and level 5 The steps are fully automated steps.

Autonomous driving is performed through mechanisms of perception, localization, path planning, and control. In addition, various companies are developing to implement recognition and path planning in autonomous driving mechanisms using artificial intelligence (AI).

For such autonomous driving, various information on roads must be preemptively collected. However, in reality, it is not easy to collect and analyze vast amounts of information in real time using only vehicle sensors. Accordingly, in order for autonomous driving to become a reality, maps with high precision that can provide various information necessary for actual autonomous driving are essential.

Here, the high-precision road map refers to a three-dimensional electronic map constructed with information on roads and surrounding topography with an accuracy of ±25 cm. In addition to general electronic map information (node information and link information necessary for route guidance), these precision maps include road width, road curvature, road slope, lane information (dotted lines, solid lines, stop lines, etc.) It is a map that includes precise information such as surface type information (crosswalks, speed bumps, shoulders, etc.), road surface mark information, sign information, facility information (traffic lights, curbs, manholes, etc.).

In order to create a map with such precision, a Mobile Mapping System (MMS), aerial photographic information, and various related data are required.

The MMS is mounted on a vehicle to measure the position of a feature around a road and acquire visual information while driving the vehicle. That is, the MMS collects GPS (Global Positioning System), Inertial Navigation System (INS), Inertial Measurement Unit (IMU), shape and information of landmarks to acquire position and attitude information of the vehicle body. It may be generated based on information collected by a camera, a LiDAR (Light Detection and Ranging) sensor, and other sensors for processing.

Among them, LiDAR can acquire 3D data representing the distance and shape of an object by emitting high-powered laser pulses and measuring the time for the laser to reflect and return to the target.

However, since the lidar used in MMS is installed in a vehicle moving on the road and acquires 3D data in real time, an error may exist as the position of the lidar changes during the process of acquiring 3D data.

In addition, MMS may have an error due to the installation position and movement of sensors even in the process of comprehensively reflecting data collected by lidar and data collected by other sensors. And, these errors have a problem of reducing the accuracy of the map with the precision generated.

Republic of Korea Patent Publication No. 10-2013-0123041, ‘Method of automatically generating an indoor map using LIDAR device’, (published on November 12, 2013)

One object of the present invention is to provide a trajectory correction method capable of minimizing errors that may occur in the process of generating a 3D map based on lidar data.

Another object of the present invention is to provide a computer program recorded on a recording medium to execute a trajectory correction method capable of minimizing errors that may occur in the process of generating a 3D map based on lidar data.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to achieve the technical problem as described above, the present invention proposes a trajectory correction method. The method includes a step of correcting, by a map generating device, an error due to a change in location for 3D point cloud data collected in real time through a LiDAR whose location is changed in real time; Estimating an expected trajectory related to the change in position based on corrected 3D point cloud data; applying, by the map generating device, curve fitting to the estimated predicted trajectory; the map The generating apparatus may include mapping an expected trajectory before curve fitting to an expected trajectory to which the curve fitting is applied.

Specifically, in the correcting of the error, an error of the 3D point cloud data according to the change in position may be corrected based on INS and GPS data collected simultaneously with the 3D point cloud data.

In the correcting of the error, fusion data may be generated by supplementing the relative position included in the INS data with the absolute position of the GPS data.

The step of correcting the error searches for fusion data closest to the position of the lidar, and based on the 3D point cloud data and time information at which the fusion data was collected, the error of the 3D point cloud data according to the conversion of the position can be corrected.

In the estimating the expected trajectory, the expected trajectory may be estimated by expressing the 3D point cloud data and the fusion data in a voxel space and approximating a point group included in each voxel with a Gaussian distribution.

In the estimating the expected trajectory, coordinates of the estimated expected trajectory may be converted into coordinates of the fusion data.

In the estimating the expected trajectory, coordinates of the estimated expected trajectory may be converted into coordinates of the fusion data through a rotation matrix and a movement matrix for the posture of the lidar.

In the applying of the curve fitting, curve fitting may be applied to the estimated trajectory through a Bezier curve fitting algorithm.

In the applying of the curve fitting, up-sampling may be performed on the predicted trajectory to which the curve fitting is applied, and a time stamp may be assigned to the up-sampled point.

In the step of applying the curve fitting, time stamps may be equally assigned to the up-sampled points at preset intervals.

In the mapping, based on the time stamp, position coordinates of points included in the predicted trajectory before the curve fitting may be modified to points included in the predicted trajectory to which the curve fitting is applied.

In the mapping, only position coordinates may be modified from points included in the predicted trajectory before the curve fitting to points included in the predicted trajectory to which the curve fitting is applied, and posture and speed data may be maintained.

In order to achieve the technical problem as described above, the present invention proposes a computer program recorded on a recording medium to execute the trajectory correction method. The program is combined with a computing device including a memory, a transceiver, and a processor that processes instructions resident in the memory, and the processor is a lidar in which the installed position changes in real time ( Correcting an error due to a change in position of 3D point cloud data collected in real time through LiDAR), wherein the processor, based on the corrected 3D point cloud data, predicts an odometry associated with the change in position ), the processor applying curve fitting to the estimated expected trajectory, the processor mapping the expected trajectory before curve fitting to the expected trajectory to which the curve fitting is applied ( In order to execute the step of mapping), it may be a computer program recorded on a recording medium.

Details of other embodiments are included in the detailed description and drawings.

According to embodiments of the present invention, errors in 3D point cloud data collected through LIDAR are corrected, and curve fitting is applied to an expected trajectory estimated through the error-corrected 3D point cloud data. By generating the map, errors occurring in the process of generating the 3D map can be minimized.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a block diagram of a map generating system according to an embodiment of the present invention.
2 is a logical configuration diagram of a map generating device according to an embodiment of the present invention.
3 is a hardware configuration diagram of a map generating device according to an embodiment of the present invention.
4 is a flowchart illustrating a method for generating a map according to an embodiment of the present invention.
5 is a diagram in which 3D point cloud data is visualized in a bird's eye view before performing error correction due to a position change of lidar according to an embodiment of the present invention.
6 is a diagram in which 3D point cloud data is visualized in a bird's eye view after performing error correction due to a position change of lidar according to an embodiment of the present invention.
7 is an exemplary diagram for explaining a trajectory correction method according to an embodiment of the present invention.
8 is an exemplary diagram for explaining an effect according to a trajectory correction method according to an embodiment of the present invention.

It should be noted that the technical terms used in this specification are only used to describe specific embodiments and are not intended to limit the present invention. In addition, technical terms used in this specification should be interpreted in terms commonly understood by those of ordinary skill in the art to which the present invention belongs, unless specifically defined otherwise in this specification, and are excessively inclusive. It should not be interpreted in a positive sense or in an excessively reduced sense. In addition, when the technical terms used in this specification are incorrect technical terms that do not accurately express the spirit of the present invention, they should be replaced with technical terms that those skilled in the art can correctly understand. In addition, general terms used in the present invention should be interpreted as defined in advance or according to context, and should not be interpreted in an excessively reduced sense.

Also, singular expressions used in this specification include plural expressions unless the context clearly indicates otherwise. In this application, terms such as 'consisting of' or 'having' should not be construed as necessarily including all of the various components or steps described in the specification, and some of the components or steps are included. It should be construed that it may not be, or may further include additional components or steps.

Also, terms including ordinal numbers such as first and second used in this specification may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.

When a component is referred to as being 'connected' or 'connected' to another component, it may be directly connected or connected to the other component, but other components may exist in the middle. On the other hand, when a component is referred to as "'directly connected' or 'directly connected' to another component, it should be understood that no other component exists in the middle.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings, but the same or similar components are given the same reference numerals regardless of reference numerals, and redundant description thereof will be omitted. In addition, in describing the present invention, if it is determined that a detailed description of a related known technology may obscure the gist of the present invention, the detailed description will be omitted. In addition, it should be noted that the accompanying drawings are only for easily understanding the spirit of the present invention, and should not be construed as limiting the spirit of the present invention by the accompanying drawings. The spirit of the present invention should be construed as extending to all changes, equivalents or substitutes other than the accompanying drawings.

On the other hand, the MMS is mounted on a vehicle to measure the position of a feature in the vicinity of a road and acquire visual information while driving the vehicle. That is, the MMS collects GPS (Global Positioning System), Inertial Navigation System (INS), Inertial Measurement Unit (IMU), shape and information of landmarks to acquire position and attitude information of the vehicle body. It may be generated based on information collected by a camera, a LiDAR (Light Detection and Ranging) sensor, and other sensors for processing.

Among them, LiDAR can acquire 3D data representing the distance and shape of an object by emitting high-powered laser pulses and measuring the time for the laser to reflect and return to the target.

However, since the lidar used in MMS is installed in a vehicle moving on the road and acquires 3D data in real time, an error may exist as the position of the lidar changes in the process of acquiring 3D data.

In addition, in the process of comprehensively reflecting data collected by the lidar and data collected by other sensors in the MMS, errors may exist depending on the installation position and movement amount of the sensors. And, these errors have a problem of reducing the accuracy of the map with the precision generated.

In order to overcome these limitations, the present invention proposes various means capable of minimizing errors generated in the process of generating a 3D map using 3D point cloud data collected through LIDAR.

Hereinafter, various embodiments of the present invention having the above characteristics will be described in more detail.

1 is a block diagram of a map generating system according to an embodiment of the present invention.

As shown in FIG. 1 , a map generating system according to an embodiment of the present invention may include a data collecting device 100 and a map generating device 200 .

Since the components of the map generation system according to one embodiment are merely functionally distinct elements, two or more components are integrated and implemented in an actual physical environment, or one component is implemented in an actual physical environment. They may be implemented separately from each other.

The data collection device 100 is fixedly installed in the vehicle 10 to collect data that can be used for 3D map generation. System, 120) and GPS (Global Positioning System, 130).

Here, the LIDAR 110 included in the data collection device 100 is fixedly installed in the vehicle 10, emits laser pulses around the vehicle 10, and targets objects located around the vehicle 10. 3D point cloud data corresponding to a 3D image of the surroundings of the vehicle 10 may be generated by detecting light reflected and returned by the vehicle 10 . Accordingly, the 3D point cloud data obtained by the lidar 110 may include a set of points obtained by reflecting a laser pulse emitted by the lidar 110 into a 3D space.

Meanwhile, the INS 120 is a system that calculates information about a relative position, real-time speed, and attitude using acceleration and angular velocity measured by a gyro sensor and an acceleration sensor, which are inertial sensors (IMUs). Since the INS 120 calculates the posture and speed by integrating the input values of the acceleration sensor and the gyro sensor, there is a disadvantage in that errors accumulate over time.

On the other hand, the GPS 130 is a radio navigation system that uses satellites to calculate the absolute position on the earth. Unlike the INS 120, the short-term navigation error is large, but the error does not accumulate over time. It has the advantage.

Accordingly, the data collection device 100 may collect the relative position, attitude, and speed from the INS 120 and the absolute position from the GPS 130 and transmit the data to the map generator 200 . Accordingly, the data collection device 100 may generate fusion data supplemented with the absolute location of the GPS 130 for the relative location collected by the map generating device 200 from the INS 120 .

With the following configuration, the map generating device 200 receives data collected from the LIDAR 110, the INS 120, and the GPS 130 from the data collection device 100, and creates a 3D map using the received data. It is a device that can be used to create

The map generating device 200 as described above is basically a device that is distinct from the data collection device 100, but may be integrated with the data collection device 100 as one device in an actual physical environment. That is, the map generating device 200 may be integrated with the data collection device 100 to collect data for generating a 3D map and generate a 3D map using the data collected by itself.

Characteristically, the map generating apparatus 200 according to an embodiment of the present invention corrects an error due to a change in position for 3D point cloud data collected in real time through LiDAR, in which the position changes in real time, Based on the calibrated 3D point cloud data, an expected trajectory related to the change in position (odometry) is estimated, curve fitting is applied to the estimated expected trajectory, and curve fitting is applied to the expected trajectory before curve fitting. An expected trajectory may be mapped.

Here, the curve fitting is an algorithm that can generate the most ideal straight line or curve that can express the estimated expected trajectory. That is, the map generating apparatus 200 according to an embodiment of the present invention may apply curve fitting to the collected 3D point cloud data to obtain interpolation and smoothing effects for the 3D point cloud data.

As such, the map generating device 200 may be any device capable of transmitting/receiving data to/from the data collection device 100 and performing calculations based on the transmitted/received data. For example, the map generating device 200 may be any one of a fixed computing device such as a desktop, workstation, or server, but is not limited thereto.

As described above, the data collection device 100 and the map generating device 200 transmit and receive data using a network in which at least one of a security line, a common wired communication network, and a mobile communication network directly connects devices. can do.

For example, public wired communication networks may include Ethernet, x Digital Subscriber Line (xDSL), Hybrid Fiber Coax (HFC), and Fiber To The Home (FTTH). It may be, but is not limited thereto. In addition, in the mobile communication network, Code Division Multiple Access (CDMA), Wideband CDMA (WCDMA), High Speed Packet Access (HSPA), Long Term Evolution, LTE) and 5th generation mobile telecommunication may be included, but is not limited thereto.

Hereinafter, the logical configuration of the map generating device 200 according to an embodiment of the present invention will be described in detail.

2 is a logical configuration diagram of a map generating device according to an embodiment of the present invention.

As shown in FIG. 2, the map generating device 200 according to an embodiment of the present invention includes a communication unit 205, an input/output unit 210, a storage unit 215, an error correction unit 220, and a trajectory estimation unit. 225, a trajectory correcting unit 230, a trajectory mapping unit 235, and a 3D map generating unit 240 may be included.

Since the components of the map generating device 200 are merely functionally distinct elements, two or more components are integrated and implemented in an actual physical environment, or one component is separated from each other in an actual physical environment. and can be implemented.

Describing each component, the communication unit 205 includes 3D point cloud data generated from the data collection device 100 using the LIDAR 110, relative position, attitude and speed values generated from the INS 120, An absolute position value generated from the GPS 130 may be received.

And, the communication unit 205 may transmit all or part of the generated 3D map to a plurality of vehicles for autonomous driving.

With the following configuration, the input/output unit 210 may receive a signal from a user through a user interface (UI) or output an operation result to the outside.

Specifically, the input/output unit 210 may receive control signals for error correction, trajectory estimation, trajectory correction, trajectory mapping, and 3D map generation from the user. That is, the input/output unit 210 generates a control signal for correcting an error of 3D point cloud data, a control signal for estimating an expected trajectory, a control signal for applying curve fitting to the estimated expected trajectory, and a curve fitting applied expected trajectory and mapping A control signal or the like for doing so may be input.

With the following configuration, the storage unit 215 may store the 3D point cloud data received through the communication unit 205 and the position, attitude, and speed values of the INS 120 and the GPS 130 . In addition, the storage unit 215 includes a program for correcting errors in 3D point cloud data, a program for estimating an expected trajectory, a program for applying curve fitting to the estimated expected trajectory, and a program for mapping the expected trajectory to which curve fitting has been applied. etc. can be entered.

Meanwhile, the data collection device 100 collects 3D point cloud data from the lidar 110 while moving through the vehicle 10 . At this time, the collected 3D point cloud data is generated based on the relative coordinates of each point. Therefore, in order to transform a plurality of 3D point cloud data into the same coordinate system, it is necessary to accurately know the odometry of the LIDAR 110 . Here, in order to know the trajectory of the lidar 110, the positional information of the lidar 110 is required.

The error correction unit 220 may correct an error due to a change in position for 3D point cloud data collected in real time through LiDAR, the position of which changes in real time. That is, the error correction unit 220 may correct an error of the 3D point cloud data according to a change in position based on the INS 120 and GPS 130 data collected simultaneously with the 3D point cloud data.

Specifically, the error correction unit 220 may generate fusion data obtained by supplementing the relative position included in the INS 120 data with the absolute position of the GPS 130 data. In addition, the error correction unit 220 searches for fusion data closest to the position of the lidar 110, and based on the 3D point cloud data and the time information at which the fusion data is collected, according to the position transformation of the lidar 110 Errors in 3D point cloud data can be corrected.

For example, the error correction unit 220 may correct errors of 3D point cloud data using the following pseudo code.

Iteration statement (repeat for points corresponding to one frame of lidar)

Search route data (INS/GPS) closest to the current lidar point

timeDiff = (Current lidar point data time) - (Current INS/GPS data time)

pt_easting = INS_easting + INS_eastingVelocity * timeDiff

pt_northing = INS_northing + INS_northingVelocity * timeDiff

pt_up = INS_up + INS_upVelocity * timeDiff

Here, INS/GPS means fusion data, and variables prefixed with INS_ can be location and speed data of fusion data.

With the following configuration, the trajectory estimator 225 may estimate an expected trajectory (odometry) of the 3D point cloud data corrected by the error correction unit 220 . Here, the expected trajectory may be a point indicating the location of each frame.

Specifically, the trajectory estimator 225 may express the 3D point cloud data and fusion data in a voxel space, and estimate the expected trajectory by approximating a point group included in each voxel with a Gaussian distribution. That is, the trajectory estimator 225 estimates the expected trajectory using a normal distribution transform (NDT) matching algorithm.

Here, the NDT algorithm processes voxel space indexing in 3D space and approximates each voxel's point group with a 3D Gaussian distribution to obtain a relatively fast calculation speed compared to the iterative closest points (ICP) algorithm. can

Thereafter, the trajectory estimator 225 may convert coordinates of the estimated expected trajectory into coordinates of the fusion data. That is, the estimated expected trajectory represents the position of the lidar 110 on the world coordinate system. Accordingly, the trajectory estimator 225 may convert the position of the LIDAR 110 on the world coordinate system into the position of the fusion data.

At this time, the trajectory estimator 225 may convert the coordinates of the expected trajectory estimated through the rotation matrix and the movement matrix for the posture of the lidar 110 into coordinates of the fusion data.

For example, the trajectory estimator 225 may convert the position of the LIDAR 110 into the position of the fusion data (INS/GPS) through Equation 1 below.

First, CurLiDARPose can be expressed as a 3x1 Matrix (roll, pitch, yaw), CurLiDARPose can be expressed as a 3x1 Matrix (easting, northing, up), and INS/GPS_Position can be expressed as a 3x1 Matrix (easting, northing, up).

[Equation 1]

INS/GPS _Position = RotationMatrix(CurLiDARPose _roll , CurLiDARPose _pitch , CuLiDARPose _yaw ) * T _L2I + CurLiDARPoseition

Here, T _L2I may be a 3x1 matrix (LiDAR to IMU translation).

At this time, CurLiDARPose can be expressed as a Rotation Matrix using Euler angles as shown in Equation 2 below.

When Z-axis rotation is set to α, y-axis rotation to β, and z-axis rotation to γ (α, β, γ: Radian)

[Equation 2]

With the following configuration, the trajectory correction unit 230 may apply curve fitting to the expected trajectory estimated by the trajectory estimating unit 225 .

That is, the map generating apparatus 200 according to an embodiment of the present invention may apply curve fitting to the collected 3D point cloud data to obtain interpolation and smoothing effects for the 3D point cloud data.

At this time, the trajectory correction unit 230 may apply curve fitting to the predicted trajectory estimated through a Bezier curve fitting algorithm. That is, the trajectory correction unit 230 may form a curve from an expected trajectory including a plurality of points. The trajectory correction unit 230 may form a curve using Equation 3 below.

[Equation 3]

Here, B(u) denotes a Bezier curve function, P _k denotes the coordinates of each point of the expected trajectory, N denotes the number of points, and u denotes a constant ratio between points.

Also, the trajectory correction unit 230 may perform up-sampling on an expected trajectory to which curve fitting is applied, and may assign a time stamp to the up-sampled points. Here, the trajectory compensator 230 may equally assign time stamps to the upsampled points at preset intervals. That is, the trajectory correction unit 230 may generate points at regular intervals based on time on the expected trajectory to which the curve fitting is applied, and may input time-related information to the generated points.

With the following configuration, the trajectory mapping unit 235 converts a point included in an expected trajectory before curve fitting to a point included in an expected trajectory to which curve fitting is applied, based on the time stamp generated by the trajectory correction unit 230, and converts the position coordinates. can be modified In this case, the trajectory mapping unit 235 may correct the location coordinates of points included in the predicted trajectory before curve fitting to points included in the predicted trajectory to which curve fitting is applied. That is, the trajectory mapping unit 235 may traverse each point of the trajectory before correction and read easting, northing, and up values of a point having the closest time stamp to correct the location coordinates of the trajectory.

With the following configuration, the 3D map generator 240 refers to the trajectory finally generated by the trajectory mapping unit 235, unifies the 3D point cloud data at each point into a standard coordinate system, and overlaps to generate a 3D map. there is.

Hereinafter, hardware for implementing logical components of the map generating apparatus 200 according to an embodiment of the present invention will be described in more detail.

3 is a hardware configuration diagram of a map generating device according to an embodiment of the present invention.

As shown in FIG. 3, the map generating device 200 includes a processor 250, a memory 255, a transceiver 260, an input/output device 265, and a data bus Bus, 270) and storage (Storage, 275) can be configured to include.

The processor 250 may implement operations and functions of the map generator 200 based on instructions according to the software 280a in which the method according to the embodiments of the present invention is resident in the memory 255 . Software 280a in which a method according to embodiments of the present invention is implemented may be loaded in the memory 255 . The transceiver 260 may transmit and receive data to and from the data collection device 100 . The input/output device 265 may receive data necessary for the operation of the map generating device 200 and output collected point cloud data and a generated 3D map. The data bus 270 is connected to the processor 250, the memory 255, the transceiver 260, the input/output device 265, and the storage 275, and is a movement path for transferring data between each component. role can be fulfilled.

The storage 275 stores an application programming interface (API), a library file, a resource file, etc. necessary for the execution of the software 280a in which the method according to the embodiments of the present invention is implemented. can be saved The storage 275 may store software 280b in which a method according to embodiments of the present invention is implemented. Also, the storage 275 may store information necessary for performing a method according to embodiments of the present invention.

According to one embodiment of the present invention, the software 280a, 280b for implementing the method of providing a guide, which resides in the memory 255 or is stored in the storage 275, is the processor 250, the location of which is changed in real time. The error due to the change in position is corrected for the 3D point cloud data collected in real time through LiDAR, the expected trajectory related to the change in position is estimated based on the corrected 3D point cloud data, and the estimated expected trajectory It may be a computer program recorded on a recording medium to execute a step of applying curve fitting to and mapping an expected trajectory before curve fitting to an expected trajectory to which curve fitting is applied.

More specifically, the processor 250 may include an Application-Specific Integrated Circuit (ASIC), another chipset, a logic circuit, and/or a data processing device. The memory 255 may include read-only memory (ROM), random access memory (RAM), flash memory, a memory card, a storage medium, and/or other storage devices. The transceiver 260 may include a baseband circuit for processing wired/wireless signals. The input/output device 265 includes an input device such as a keyboard, a mouse, and/or a joystick, and a Liquid Crystal Display (LCD), an Organic LED (OLED), and/or a liquid crystal display (LCD). Alternatively, an image output device such as an active matrix OLED (AMOLED) may include a printing device such as a printer or a plotter.

When the embodiments included in this specification are implemented as software, the above-described method may be implemented as a module (process, function, etc.) that performs the above-described functions. A module may reside in memory 255 and be executed by processor 250 . The memory 255 may be internal or external to the processor 250 and may be connected to the processor 250 by various well-known means.

Each component shown in FIG. 3 may be implemented by various means, eg, hardware, firmware, software, or a combination thereof. In the case of hardware implementation, one embodiment of the present invention includes one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), FPGAs ( Field Programmable Gate Arrays), processors, controllers, microcontrollers, microprocessors, etc.

In addition, in the case of implementation by firmware or software, an embodiment of the present invention is implemented in the form of a module, procedure, function, etc. that performs the functions or operations described above, and is stored on a recording medium readable through various computer means. can be recorded. Here, the recording medium may include program commands, data files, data structures, etc. alone or in combination. Program instructions recorded on the recording medium may be those specially designed and configured for the present invention, or those known and usable to those skilled in computer software. For example, recording media include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks), floptical It includes hardware devices specially configured to store and execute program instructions, such as magneto-optical media, such as a floptical disk, and ROM, RAM, flash memory, and the like. Examples of program instructions may include high-level language codes that can be executed by a computer using an interpreter or the like as well as machine language codes generated by a compiler. These hardware devices may be configured to operate as one or more pieces of software to perform the operations of the present invention, and vice versa.

Hereinafter, a method for generating a map according to an embodiment of the present invention will be described in detail.

4 is a flowchart illustrating a method for generating a map according to an embodiment of the present invention.

Referring to FIG. 4 , the map generating apparatus 200 according to an embodiment of the present invention targets 3D point cloud data collected in real time through LiDAR, the location of which changes in real time, according to the change in location. Errors can be corrected (S100). That is, the map generating device 200 may correct an error of the 3D point cloud data according to a change in location based on the INS 120 and GPS 130 data collected simultaneously with the 3D point cloud data.

Specifically, the map generating device 200 may generate fusion data by supplementing the relative position included in the INS data with the absolute position of the GPS data. In addition, the map generating device 200 searches for fusion data closest to the location of the lidar 110, and based on the 3D point cloud data and the time information at which the fusion data was collected, the error of the 3D point cloud data according to the location conversion can be corrected.

Next, the map generating apparatus 200 may estimate an expected trajectory (odometry) of the 3D point cloud data corrected in step S100 (S200).

In detail, the map generating apparatus 200 may express 3D point cloud data and fusion data in a voxel space, and estimate a predicted trajectory by approximating a point group included in each voxel with a Gaussian distribution. That is, the map generator 200 estimates an expected trajectory using a normal distribution transform (NDT) matching algorithm.

Then, the map generating device 200 may convert the coordinates of the estimated expected trajectory into coordinates of the fusion data. That is, the estimated expected trajectory represents the position of the lidar 110 on the world coordinate system. Accordingly, the map generating device 200 may convert the location of the LIDAR 110 on the world coordinate system to the location of the fusion data using the location relationship with the fusion data.

In this case, the map generating device 200 may convert the coordinates of the predicted trajectory estimated through the rotation matrix and the movement matrix for the posture of the LIDAR 110 into coordinates of the fusion data.

Next, the map generating device 200 may apply curve fitting to the expected trajectory estimated in step S200 (S300). That is, the map generating apparatus 200 may obtain interpolation and smoothing effects for the 3D point cloud data by applying curve fitting to the collected 3D point cloud data. In this case, the map generating device 200 may apply curve fitting to the predicted trajectory estimated through a Bezier curve fitting algorithm.

Also, the map generating apparatus 200 may perform up-sampling on an expected trajectory to which curve fitting is applied, and may assign a time stamp to the up-sampled points. Here, the map generating device 200 may equally assign time stamps to the up-sampled points at preset intervals. That is, the map generating device 200 may generate points at regular intervals based on time on the predicted trajectory to which the curve fitting is applied, and may input time-related information to the generated points.

Next, the map generating device 200 may modify the location coordinates of points included in the predicted trajectory before curve fitting to points included in the predicted trajectory to which curve fitting is applied, based on the time stamp generated in step S300. In this case, the map generating device 200 may modify only the positional coordinates of points included in the predicted trajectory before curve fitting to points included in the predicted trajectory to which curve fitting is applied, and maintain posture and speed data.

In addition, the map generating apparatus 200 may generate a 3D map by unifying the 3D point cloud data at each point into a standard coordinate system and overlapping the 3D point cloud data at each point with reference to the trajectory finally generated in step S400.

5 and 6 are views visualizing 3D point cloud data in a bird's eye view before and after performing error correction due to a position change of lidar according to an embodiment of the present invention.

Meanwhile, the lidar 110 of the data collection device 100 according to an embodiment of the present invention is mounted on the vehicle 10 and collects 3D point cloud data while moving.

However, in the process of data processing, processing is performed under the assumption that one frame is located at the same location. That is, within one frame, points are arranged without considering the positional change of the lidar 110.

Accordingly, as shown in a of FIG. 5 , the starting point and the last point of the 3D point cloud data within one frame appear to coincide. However, in practice, 3D point cloud data does not coincide with the starting point and the last point according to the location change.

Accordingly, the map generating device 200 according to an embodiment of the present invention, based on the INS 120 and GPS 130 data collected at the same time as the 3D point cloud data, 3D data according to the position change of the lidar 110 Errors in point cloud data can be corrected.

Specifically, the map generating device 200 may generate fusion data by supplementing the relative position included in the INS 120 data with the absolute position of the GPS 130 data. In addition, the map generating device 200 searches for fusion data closest to the location of the lidar 110, and based on the 3D point cloud data and the time information at which the fusion data was collected, the error of the 3D point cloud data according to the location conversion can be corrected.

That is, as shown in b of FIG. 6 , the map generating device 200 may obtain 3D point cloud data considering the positional change of the lidar 110 through error correction.

7 is an exemplary diagram for explaining a trajectory correction method according to an embodiment of the present invention.

Referring to FIG. 7 , the map generating apparatus 200 may obtain a curve fitting result by applying curve fitting to an estimated localizing result.

That is, the map generating apparatus 200 may obtain interpolation and smoothing effects for 3D point cloud data by applying curve fitting to the collected predicted trajectory (localizing result) of the 3D point cloud form.

Also, the map generating apparatus 200 may perform up-sampling on a localizing result to which curve fitting is applied. Also, the map generating device 200 may assign a time stamp to the up-sampled point (sampling point from curve). Here, the map generating apparatus 200 may equally assign time stamps to up-sampled points (sampling point from curve) at preset intervals.

Further, the map generating device 200 converts a point (INS/GPS position point) included in the predicted trajectory before curve fitting to a point (sampling point from curve) included in the predicted trajectory to which curve fitting is applied, based on the generated time stamp. Position coordinates can be modified (position change).

8 is an exemplary diagram for explaining an effect according to a trajectory correction method according to an embodiment of the present invention.

According to embodiments of the present invention, errors in 3D point cloud data collected through LIDAR are corrected, and curve fitting is applied to an expected trajectory estimated through the error-corrected 3D point cloud data. By generating the map, errors occurring in the process of generating the 3D map can be minimized.

8 is a side view visualization of the results when a 3D point cloud map is generated by the trajectory correction method according to an embodiment of the present invention and when it is not applied.

That is, as shown in FIG. 8 , as the map generating device 200 performs the trajectory correction method, it is possible to obtain data (b) reflecting the positioning result, and to compare with the data (c) before correction, the reference data Results very similar to (a) were obtained.

As described above, although preferred embodiments of the present invention have been disclosed in the present specification and drawings, it is in the technical field to which the present invention belongs that other modified examples based on the technical spirit of the present invention can be implemented in addition to the embodiments disclosed herein. It is self-evident to those skilled in the art. In addition, although specific terms have been used in the present specification and drawings, they are only used in a general sense to easily explain the technical content of the present invention and help understanding of the present invention, but are not intended to limit the scope of the present invention. Accordingly, the foregoing detailed description should not be construed as limiting in all respects and should be considered illustrative. The scope of the present invention should be selected by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.
Assignment number: SC210053
Project management (specialized) agency name: Seoul Business Agency
Research project name: 2021 Seoul Innovation Challenge (preliminary)
Research project title: Urban change detection platform using mobility service
Name of the organization performing the task: Mobiltech Co., Ltd.
Research period: 2021. 08. 01 ~ 2021. 11. 30

Data Collector: 100 Map Generator: 200
Communication unit: 205 I/O unit: 210
Storage unit: 215 Error correction unit: 220
Trajectory estimation unit: 225 Trajectory correction unit: 230
Trajectory mapping unit: 235 3D map generation unit: 240

Claims (10)

Correcting, by a map generating device, an error due to a change in location of 3D point cloud data collected in real time through a LiDAR whose location changes in real time;
estimating, by the map generator, an expected trajectory (odometry) related to the change in position based on the corrected 3D point cloud data;
applying, by the map generating device, curve fitting to the estimated trajectory;
mapping, by the map generating device, an expected trajectory before curve fitting to an expected trajectory to which the curve fitting is applied;
The step of correcting the error is
Based on INS and GPS data collected simultaneously with the 3D point cloud data, an error of the 3D point cloud data according to the change in position is corrected,
The step of correcting the error is
Characterized in that the fusion data is generated by supplementing the relative position included in the INS data with the absolute position of the GPS data,
The step of correcting the error is
Searching for fusion data closest to the position of the lidar, and correcting an error of the 3D point cloud data according to the conversion of the position based on the 3D point cloud data and the time information at which the fusion data was collected, ,
The step of estimating the expected trajectory is
Characterized in that the expected trajectory is estimated by expressing the 3D point cloud data and the fusion data in a voxel space and approximating a point group included in each voxel with a Gaussian distribution;
The step of estimating the expected trajectory is
Characterized in that the coordinates of the estimated expected trajectory are converted to the coordinates of the fusion data,
The step of estimating the expected trajectory is
Characterized in that the coordinates of the estimated expected trajectory are converted into coordinates of the fusion data through a rotation matrix and a movement matrix for the posture of the lidar,
Characterized in that the fusion data coordinates are converted based on the following Equation 1, the trajectory correction method.
[Equation 1]
INS/GPS _Position = RotationMatrix(CurLiDARPose _roll , CurLiDARPose _pitch , CuLiDARPose _yaw ) * T _L2I + CurLiDARPoseition
(Where CurLiDARPose is a 3x1 Matrix (roll, pitch, yaw), CurLiDARPose is a 3x1 Matrix (easting, northing, up), INS/GPS_Position is a 3x1 Matrix (easting, northing, up), and T _L2I is a 3x1 matrix ( LiDAR to IMU translation)
The method of claim 1, wherein applying the curve fitting comprises:
Characterized in that curve fitting is applied to the estimated expected trajectory through a Bezier Curve Fitting algorithm, the trajectory correction method.
3. The method of claim 2, wherein applying the curve fitting comprises:
Characterized in that, up-sampling is performed on the expected trajectory to which the curve fitting is applied, and a time stamp is given to the up-sampled point.
memory;
transceiver; and
In combination with a computing device configured to include a processor for processing instructions resident in the memory,
Correcting, by the processor, an error due to a change in the position of 3D point cloud data collected in real time through a LiDAR in which the installed position changes in real time;
estimating, by the processor, an expected trajectory (odometry) related to the change in position based on the corrected 3D point cloud data;
applying, by the processor, curve fitting to the estimated trajectory;
Performing, by the processor, mapping an expected trajectory before the curve fitting to an expected trajectory to which the curve fitting is applied,
The step of correcting the error is
Based on INS and GPS data collected simultaneously with the 3D point cloud data, an error of the 3D point cloud data according to the change in position is corrected,
The step of correcting the error is
Characterized in that the fusion data is generated by supplementing the relative position included in the INS data with the absolute position of the GPS data,
The step of correcting the error is
Searching for fusion data closest to the position of the lidar, and correcting an error of the 3D point cloud data according to the conversion of the position based on the 3D point cloud data and the time information at which the fusion data was collected, ,
The step of estimating the expected trajectory is
Characterized in that the expected trajectory is estimated by expressing the 3D point cloud data and the fusion data in a voxel space and approximating a point group included in each voxel with a Gaussian distribution;
The step of estimating the expected trajectory is
Characterized in that the coordinates of the estimated expected trajectory are converted to the coordinates of the fusion data,
The step of estimating the expected trajectory is
Characterized in that the coordinates of the estimated expected trajectory are converted into coordinates of the fusion data through a rotation matrix and a movement matrix for the posture of the lidar,
Characterized in that the fusion data coordinates are converted based on Equation 1 below, a computer program recorded on a recording medium.
[Equation 1]
INS/GPS _Position = RotationMatrix(CurLiDARPose _roll , CurLiDARPose _pitch , CuLiDARPose _yaw ) * T _L2I + CurLiDARPoseition
(Where CurLiDARPose is a 3x1 Matrix (roll, pitch, yaw), CurLiDARPose is a 3x1 Matrix (easting, northing, up), INS/GPS_Position is a 3x1 Matrix (easting, northing, up), and T _L2I is a 3x1 matrix ( LiDAR to IMU translation)
5. The method of claim 4, wherein applying the curve fitting comprises:
A computer program recorded on a recording medium, characterized in that curve fitting is applied to the estimated expected trajectory through a Bezier Curve Fitting algorithm.
6. The method of claim 5, wherein applying the curve fitting comprises:
A computer program recorded on a recording medium, characterized in that performing up-sampling on the predicted trajectory to which the curve fitting is applied, and assigning a time stamp to the up-sampled point. delete delete delete delete

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