CN113093221A - Generation method and device of grid-occupied map - Google Patents
Generation method and device of grid-occupied map Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
Abstract
The embodiment of the invention provides a method and a device for generating a grid occupation map. According to the method and the device, through obtaining the multi-frame laser radar point cloud data collected by the target vehicle within the preset time period, in the collection process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion, the multi-frame laser radar point cloud data is converted into the world coordinate system from the grid-occupied map coordinate system to obtain the multi-frame target data, the multi-frame target data is subjected to accumulation processing to obtain the target-occupied grid map based on the world coordinate system, the grid-occupied map containing historical information can be generated, the accuracy of the grid-occupied map is improved, and the applicability of the grid-occupied map in a real scene is improved.
Description
Technical Field
The invention relates to the technical field of unmanned driving, in particular to a method and a device for generating a grid-occupied map.
Background
In the field of unmanned driving, Mapping (Mapping) is a key technology. Among them, the occupancy grid map is used to indicate the environment around the vehicle, and plays an important role in realizing unmanned driving.
During the driving process of the unmanned vehicle, a function similar to human vision is needed to sense the surrounding environment, particularly which areas are occupied by obstacles and which areas are free of obstacles, so as to determine the passing path of the vehicle. The occupancy grid map is a function that is used to replace human vision, sensing the presence of surrounding obstacles, in order to provide a feasible travel path for the unmanned vehicle.
In the related art, an unmanned vehicle generates an occupation grid map by using collected single-frame laser radar point cloud data, and a feasible driving path is predicted on the basis of the occupation grid map. The occupation grid map generated in the mode is low in accuracy, and the applicability of the occupation grid map in a real scene is low.
Disclosure of Invention
In order to overcome the problems in the related art, the invention provides a method and a device for generating a grid-occupied map, which improve the accuracy of the grid-occupied map and the applicability of the grid-occupied map in a real scene.
According to a first aspect of the embodiments of the present invention, there is provided a method for generating an occupancy grid map, including:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
Illustratively, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
Illustratively, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
Illustratively, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
Illustratively, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
Exemplarily, the method further comprises the following steps:
and predicting the driving path of the target vehicle according to the target occupation grid map.
According to a second aspect of the embodiments of the present invention, there is provided an apparatus for generating an occupancy grid map, including:
the acquisition module is used for acquiring multi-frame laser radar point cloud data acquired by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
the conversion module is used for converting the multi-frame laser radar point cloud data from an occupied grid map coordinate system to a world coordinate system to obtain multi-frame target data;
and the processing module is used for accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system.
Exemplarily, the processing module is specifically configured to:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
Exemplarily, the processing module is specifically configured to:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
Exemplarily, the processing module is specifically configured to:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
Exemplarily, the processing module is specifically configured to:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
Exemplarily, the method further comprises the following steps:
and the prediction module is used for predicting the running path of the target vehicle according to the target occupation grid map.
According to a third aspect of embodiments of the present invention, there is provided an electronic device comprising a memory and a processor, wherein:
the memory to store computer-executable instructions;
the processor is configured to read computer-executable instructions on the memory and execute the instructions to implement the method of any of the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a computer readable storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the method of any one of the first aspect.
According to a fifth aspect of embodiments of the present invention, there is provided an unmanned system for deployment on a target vehicle, the system comprising a lidar and an electronic device, wherein:
the laser radar is used for collecting multi-frame laser radar point cloud data for a target vehicle within a preset time period;
the electronic device is configured to:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the method and the device, through acquiring the multi-frame laser radar point cloud data acquired by the target vehicle within the preset time period, in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrated motion of the target vehicle is regarded as grid-integrated motion, the multi-frame laser radar point cloud data is converted into a world coordinate system from a grid-occupied map coordinate system to obtain multi-frame target data, the multi-frame target data is subjected to accumulation processing to obtain a target-occupied grid map based on the world coordinate system, the grid-occupied map containing historical information can be generated, the accuracy of the grid-occupied map is improved, and the applicability of the grid-occupied map in a real scene is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the specification.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present specification and together with the description, serve to explain the principles of the specification.
Fig. 1 is a flowchart illustrating a method for generating an occupation grid map according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of a generation apparatus for an occupation grid map according to an embodiment of the present invention.
Fig. 3 is a hardware structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of embodiments of the invention, as detailed in the following claims.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of embodiments of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used to describe various information in embodiments of the present invention, the information should not be limited by these terms. These terms are only used to distinguish one type of information from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present invention. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
For a point in the scale map, it has either an obstacle (in this case an Occupied state, usually denoted by 1) or no obstacle (in this case a Free state, usually denoted by 0). In an occupancy grid map, for a point, the state value of the point is typically generated according to the probability that the point is an Occupied state and the probability that the point is a Free state. In the occupancy grid map, a larger state value of one point indicates more certainty that it is an Occupied state, and conversely, a smaller state value indicates more certainty that it is a Free state.
A lidar is typically mounted on the unmanned vehicle. The laser radar is used for collecting point cloud data, namely the laser radar point cloud data. The principle of collecting point cloud data by the laser radar is as follows:
the laser radar comprises a plurality of laser sensors, each laser sensor emits a laser beam in a fixed direction, the emitted laser beams encounter obstacles and are reflected, so that the time difference between the emission and the return of the laser beams can be obtained, and the time difference is multiplied by the speed to be divided by two, so that the distance from the laser sensor to the nearest obstacle in the direction is obtained. And constructing the occupation grid map according to the direction information corresponding to the laser sensor and the distance information of the nearest barrier.
However, the point cloud data collected by the lidar is noisy, i.e., the point cloud data collected by the lidar may be "false data" rather than real data. For example, laser light can be reflected when encountering large dust, and corresponding point cloud data can be obtained. However, the dust is not a real obstacle, which may be a position further behind the dust, or there may be no obstacle in the direction corresponding to the beam of laser light. However, the occupancy grid map generated based on the acquired point cloud data indicates that an obstacle exists at a position closer in the direction. In this way, when a travel path is selected for the unmanned vehicle, the path that actually exists in that direction is excluded, resulting in the possibility that the unmanned vehicle may select another path that is farther away from the path.
In order to solve the above problem, an embodiment of the present invention provides a method for generating an occupancy grid map.
The method for generating the grid-occupied map according to the present invention will be described in detail below with reference to examples.
Fig. 1 is a flowchart illustrating a method for generating an occupation grid map according to an embodiment of the present invention. As shown in fig. 1, in this embodiment, the method for generating the occupancy grid map may include:
s101, acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; and in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion.
And S102, converting the multi-frame laser radar point cloud data from the occupied grid map coordinate system to the world coordinate system to obtain multi-frame target data.
And S103, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system.
In this embodiment, the length of the preset time period may be set according to the application requirement.
The unmanned vehicle is usually provided with a laser radar, and point cloud data can be collected in real time.
Wherein the collected lidar point cloud data is based on occupying a grid map coordinate system. The position of the origin occupying the grid map coordinate system changes as the position of the vehicle changes. Thus, the occupancy grid map coordinate system is different when the unmanned vehicle is in different positions. Since the unmanned vehicle is moving at all times during travel, the occupancy grid map coordinate system is changing at all times. This results in non-uniform coordinate systems of the point cloud maps acquired by the unmanned vehicle at different times during the driving process.
The unmanned vehicle is usually provided with a positioning module, and the positioning module can position the unmanned vehicle, namely, the position of the unmanned vehicle in a world map coordinate system is given.
The occupied grid map is a grid as a minimum unit of display. Each grid has a range. For example, a grid may range in size from 10 centimeters by 10 centimeters. However, during the movement of the vehicle, the movement distance is not full in many cases, for example, the vehicle runs 10.6 grids, and the situation is called non-full movement. In this embodiment, the non-full-page movement of the vehicle is regarded as full-page movement. For example, if the vehicle has traveled 10.6 grids, the vehicle position is considered to have moved 10 grids, i.e., rounded down. Of course, in other embodiments, the non-grid movement may be changed into the grid movement in a manner of rounding up.
In this embodiment, the multi-frame lidar point cloud data is converted from the grid map occupying coordinate system to the world coordinate system to obtain multi-frame target data, and the lidar point cloud data based on different grid occupying coordinate systems can be converted into data in a unified world coordinate system to provide a basis for accumulation in the following steps.
For example, assume that 10 frames of point cloud data are acquired within 10 seconds. For the same point, the coordinates occupying the grid coordinate system of the point in the 10 frames of point cloud data are different, and cannot be accumulated. After the 10 frames of point cloud data are converted into a world coordinate system from the occupied grid map coordinate system, the same points in the obtained 10 frames of target data have the same world coordinate system coordinates, and thus, the 10 frames of point cloud data can be accumulated.
In this embodiment, the target-occupied grid map obtained by accumulating the multi-frame target data includes historical data information, and compared with the occupied grid map generated based on single-frame point cloud data, the method and the device for determining the state of the grid in the target-occupied grid map can effectively eliminate the interference of noise data, and thus can more accurately determine the state of the grid in the target-occupied grid map.
For example, for a certain point in a grid map occupied by a target, only one frame of target data shows that the point has an obstacle in 10 frames of target data corresponding to 10 frames of point cloud data acquired within 10 seconds, and the other 9 frames of target data all show that the point has no obstacle. The data showing that there is an obstacle at the point is highly likely to be noise data (e.g., the laser beam happens to be reflected by dust) so that the noise data can be excluded, while the state of the point is determined to be obstacle-free from non-noise data of the other 9 frames of target data. If an occupancy grid map is generated from a single frame of point cloud data with noisy data, the state of the point may be determined to be an obstacle, which is clearly inaccurate.
In one example, accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system may include:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
For example, taking the aforementioned 10-frame target data as an example, it is assumed that each grid has an obstacle set to 1 and no obstacle set to 0. The 10 frames of target data correspond to a point (x) in the world coordinate system0，y0) Is 1, 0, respectively, then according to this embodiment, point (x) is0，y0) The value on the target occupancy grid map is 3. This indicates that there are 3 times of detection of the point with the obstacle and 7 times of detection of the point without the obstacle in 10 statistics. The condition of whether obstacles exist in the point is reflected more comprehensively, so that more accurate basis is provided for the driving path planning of the vehicle. For example, if all the grids having a numerical value of 3 or less are allowed to pass through as the grid of the travel route, the probability that the grid having the larger data is selected is set to be smaller and the probability that the grid having the smaller data is selected is set to be larger in the final selection. This may improve the accuracy of the path planning。
In one example, accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system may include:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
The preset threshold value can be set according to specific application requirements.
For example, still taking the aforementioned 10 frames of target data as an example, assume that the preset threshold is 4; the first value is 0, indicating no obstacle; the second value is 1, indicating that an obstacle is present. For a point (x) in the world coordinate system0，y0) If the sum value is 3, the target occupies a point (x) on the grid map since the sum value is less than the preset threshold value 40，y0) Is set to 0. Therefore, the values of the grid points are comprehensively determined according to the multi-frame point cloud data, and the values can be more accurate.
In one example, accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system may include:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
For example, still taking the aforementioned 10 frames of target data as an example, assuming that the designated number is 3 and the preset value is 1, the 10 frames of target data correspond to a point (x) in the world coordinate system0，y0) The data of (1) are 1, 0, respectively. The target occupies a point (x) on the grid map0，y0) Is 0, the first updated value is 1, the second updated value is 2, the third updated value is 3, the fourth updated value is 3 (at this time, 0 (indicating no obstacle at the point) is counted for 1 time), the fifth updated value is 3 (at this time, 0 (indicating no obstacle at the point) is counted for 2 consecutive times), the sixth updated value is 2 (at this time, 0 (indicating no obstacle at the point) is counted for 3 consecutive times, 1 is subtracted from the intermediate value 3 to obtain an updated value of 2), the seventh updated value is 2 (at this time, 0 (indicating no obstacle at the point) is counted again for 1 time), the eighth updated value is 2 (at this time, 0 (indicating no obstacle at the point) is counted for 2 consecutive times), the ninth updated value is 1 (at this time, 0 (indicating no obstacle at the point) is counted for 3 consecutive times, 1 is subtracted from the intermediate value 2 to obtain an updated value of 1, the tenth updated value is 1 (at this point 0 (indicating no obstacle at this point) is counted again 1 time). The final target occupies a point (x) on the grid map0，y0) Has a value of 1.
In this embodiment, updating the value of the point on the target occupancy grid map according to the difference includes:
when the difference value is greater than or equal to 0, updating the value of the point on the target occupancy grid map to the difference value;
when the difference value is less than 0, the value of the point on the target occupancy grid map is updated to 0.
That is, when the difference value is greater than or equal to 0, the value of the point on the target occupancy grid map is equal to the difference value; when the difference is less than 0, the value of the point on the target occupancy grid map is equal to 0.
In one example, accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system may include:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
For example, still taking the aforementioned 10 frames of target data as an example, assuming that the designated number is 3 and the preset value is 1, the 10 frames of target data correspond to a point (x) in the world coordinate system0，y0) The data of (1) are 1, 0, respectively. The target occupies a point (x) on the grid map0，y0) The initial value of (1) after the first update, the value after the second update is 2, the value after the third update is 3, the value after the fourth update is 3 (at this time, 0 (indicating no obstacle at the point) is counted for 1 time), the value after the fifth update is 3 (at this time, 0 (indicating no obstacle at the point) is counted for 2 consecutive times), the value after the sixth update is 0 (at this time, 0 (indicating no obstacle at the point) is counted for 3 consecutive times), the value after the seventh update is 0 (at this time, 0 (indicating no obstacle at the point) is counted again for 4 consecutive times), the value after the eighth update is 0 (at this time, 0 (indicating no obstacle at the point) is counted for 5 consecutive times), the value after the ninth update is 0 (at this time, indicating no obstacle at the point) is counted again for 6 consecutive times), and the value after the tenth update is 0 (at this time, 0 (indicating no obstacle) is counted again for 7 consecutive times). Then it is the mostThe final object occupies a point (x) on the grid map0，y0) The value of (d) is 0.
It should be noted that the above embodiments may be used in combination, for example, after all the updates are completed, the value occupying the midpoint of the grid map may be reset according to the preset threshold in the foregoing embodiments.
It should be noted that the above listed data are only examples for illustrating the principle of the embodiment and are not used to limit the present invention.
In one example, the method may further comprise:
and predicting the driving path of the target vehicle according to the target occupation grid map.
For example, at least one candidate driving route is predicted according to the grid displayed without obstacles in the target occupied grid map, and then another technology is combined to finally determine one of the at least one candidate driving route as an actual driving route.
According to the method for generating the grid-occupied map, provided by the embodiment of the invention, through acquiring the multi-frame laser radar point cloud data acquired by a target vehicle in a preset time period, in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrated motion of the target vehicle is regarded as grid-integrated motion, the multi-frame laser radar point cloud data is converted from a grid-occupied map coordinate system to a world coordinate system to obtain multi-frame target data, and the multi-frame target data is accumulated to obtain the target grid-occupied map based on the world coordinate system, so that the grid-occupied map containing historical information can be generated, the accuracy of the grid-occupied map is improved, and the applicability of the grid-occupied map in a real scene is improved.
Based on the above method embodiment, the embodiment of the present invention further provides corresponding apparatus, device, and storage medium embodiments.
Fig. 2 is a functional block diagram of a generation apparatus for an occupation grid map according to an embodiment of the present invention. As shown in fig. 2, in this embodiment, the generating device of the occupancy grid map may include:
the acquisition module 210 is configured to acquire multi-frame lidar point cloud data acquired by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
the conversion module 220 is configured to convert the multi-frame lidar point cloud data from an occupied grid map coordinate system to a world coordinate system to obtain multi-frame target data;
and the processing module 230 is configured to perform accumulation processing on the multiple frames of target data to obtain a grid map occupied by the target based on the world coordinate system.
In one example, the processing module 230 is specifically configured to:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
In one example, the processing module 230 is specifically configured to:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
In one example, the processing module 230 is specifically configured to:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In one example, the processing module is specifically configured to:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In one example, the method may further include:
and the prediction module is used for predicting the running path of the target vehicle according to the target occupation grid map.
The embodiment of the invention also provides the electronic equipment. Fig. 3 is a hardware structure diagram of an electronic device according to an embodiment of the present invention. The electronic apparatus of the present embodiment may be mounted on an unmanned vehicle. As shown in fig. 3, the electronic apparatus includes: an internal bus 301, and a memory 302, a processor 303, and an external interface 304, which are connected through the internal bus, wherein,
the memory 302 is used for storing machine readable instructions corresponding to the generation logic of the occupation grid map;
the processor 303 is configured to read the machine-readable instructions in the memory 302 and execute the instructions to implement the following operations:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In one example, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In an exemplary implementation, the method further includes:
and predicting the driving path of the target vehicle according to the target occupation grid map.
The embodiment of the invention also provides an unmanned system, which comprises any one of the electronic devices.
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the following operations:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
In an exemplary implementation process, the accumulating the multiple frames of target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In one example, accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system, includes:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
In an exemplary implementation, the method further includes:
and predicting the driving path of the target vehicle according to the target occupation grid map.
The embodiment of the invention also provides an unmanned system, which is deployed on a target vehicle and comprises a laser radar and electronic equipment, wherein:
the laser radar is used for collecting multi-frame laser radar point cloud data for a target vehicle within a preset time period;
the electronic device is configured to:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
For the device and apparatus embodiments, as they correspond substantially to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, wherein the modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in the specification. One of ordinary skill in the art can understand and implement it without inventive effort.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Other embodiments of the present description will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This specification is intended to cover any variations, uses, or adaptations of the specification following, in general, the principles of the specification and including such departures from the present disclosure as come within known or customary practice within the art to which the specification pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the specification being indicated by the following claims.
It will be understood that the present description is not limited to the precise arrangements described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.
The above description is only a preferred embodiment of the present disclosure, and should not be taken as limiting the present disclosure, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (10)
1. A method of generating an occupancy grid map, comprising:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
2. The method of claim 1, wherein accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system comprises:
and for any point on the target occupation grid map, adding data corresponding to the point in the multi-frame target data, and taking the obtained sum value as the value of the point on the target occupation grid map.
3. The method of claim 1, wherein accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system comprises:
adding data corresponding to the points in the multi-frame target data to obtain a sum value for any point on the grid map occupied by the target;
if the sum value is smaller than a preset threshold value, setting the value of the point on the target occupation grid map as a first value;
and if the sum value is greater than or equal to a preset threshold value, setting the value of the point on the target occupation grid map as a second value.
4. The method of claim 1, wherein accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system comprises:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the data in the multi-frame target data, the acquisition time of which is earlier than that of the current frame target data, indicates that no obstacle exists at the point by the continuous specified number of data until the current frame target data, subtracting a preset value from the intermediate value to obtain a difference value, and updating the value of the point on the target occupation grid map according to the difference value; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
5. The method of claim 1, wherein accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system comprises:
setting an initial value of any point on the target occupation grid map as 0 for the point;
according to the acquisition time sequence of the multi-frame target data, updating the values of the points on the target occupation grid map as follows:
calculating the sum of the values of the points on the target occupation grid map after the current frame target data and the previous frame target data are updated as an intermediate value;
if the acquisition time of the multi-frame target data is earlier than that of the current frame target data until the current frame target data, and the continuously specified number of data indicates that no obstacle exists at the point, updating the value of the point on the target occupation grid map to be 0; otherwise, updating the value of the point on the target occupancy grid map to the intermediate value.
6. The method of claim 1, further comprising:
and predicting the driving path of the target vehicle according to the target occupation grid map.
7. An apparatus for generating an occupancy grid map, comprising:
the acquisition module is used for acquiring multi-frame laser radar point cloud data acquired by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
the conversion module is used for converting the multi-frame laser radar point cloud data from an occupied grid map coordinate system to a world coordinate system to obtain multi-frame target data;
and the processing module is used for accumulating the multi-frame target data to obtain a target occupation grid map based on a world coordinate system.
8. An electronic device comprising a memory and a processor, wherein:
the memory to store computer-executable instructions;
the processor is used for reading the computer executable instructions on the memory and executing the instructions to realize the method of any one of claims 1 to 6.
9. A computer-readable storage medium, having a computer program stored thereon, wherein the program, when executed by a processor, implements the method of any of claims 1-6.
10. An unmanned system for deployment on a target vehicle, the system comprising a lidar and an electronic device, wherein:
the laser radar is used for collecting multi-frame laser radar point cloud data for a target vehicle within a preset time period;
the electronic device is configured to:
acquiring multi-frame laser radar point cloud data collected by a target vehicle within a preset time period; in the acquisition process of the multi-frame laser radar point cloud data, the non-grid-integrity motion of the target vehicle is regarded as grid-integrity motion;
converting the multi-frame laser radar point cloud data from a grid map occupying coordinate system to a world coordinate system to obtain multi-frame target data;
and accumulating the multi-frame target data to obtain a target occupying grid map based on a world coordinate system.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115981344A (en) * | 2023-02-06 | 2023-04-18 | 联通智网科技股份有限公司 | Automatic driving method and device |
| EP4123338A3 (en) * | 2021-07-21 | 2023-04-19 | Hyundai Mobis Co., Ltd. | Apparatus and method for monitoring surrounding environment of vehicle |
| WO2023087202A1 (en) * | 2021-11-18 | 2023-05-25 | 华为技术有限公司 | Motion state estimation method and apparatus |
| WO2023173898A1 (en) * | 2022-03-16 | 2023-09-21 | 华为技术有限公司 | Occupancy grid map generation method and apparatus |
| WO2024070558A1 (en) * | 2022-09-30 | 2024-04-04 | 株式会社小松製作所 | Work site detection system, and work site detection method |
Citations (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101413806A (en) * | 2008-11-07 | 2009-04-22 | 湖南大学 | Mobile robot grating map creating method of real-time data fusion |
| KR20110080395A (en) * | 2010-01-05 | 2011-07-13 | 주식회사 코아로직 | Motion detection device and method, and image processing apparatus comprising the same device |
| CN104865578A (en) * | 2015-05-12 | 2015-08-26 | 上海交通大学 | Indoor parking lot high-precision map generation device and method |
| DE102015201566A1 (en) * | 2015-01-29 | 2016-08-04 | Conti Temic Microelectronic Gmbh | PICTURE IDENTIFICATION, PICTURE IDENTIFICATION SYSTEM AND VEHICLE CONTROL SYSTEM |
| KR20170001148A (en) * | 2015-06-25 | 2017-01-04 | 현대자동차주식회사 | System and Method for writing Occupancy Grid Map of sensor centered coordinate system using laser scanner |
| US20170278221A1 (en) * | 2016-03-22 | 2017-09-28 | Samsung Electronics Co., Ltd. | Method and apparatus of image representation and processing for dynamic vision sensor |
| CN109085838A (en) * | 2018-09-05 | 2018-12-25 | 南京理工大学 | A kind of dynamic barrier rejecting algorithm based on laser positioning |
| CN109186586A (en) * | 2018-08-23 | 2019-01-11 | 北京理工大学 | One kind towards dynamically park environment while position and mixing map constructing method |
| CN109829386A (en) * | 2019-01-04 | 2019-05-31 | 清华大学 | Intelligent vehicle based on Multi-source Information Fusion can traffic areas detection method |
| CN110008851A (en) * | 2019-03-15 | 2019-07-12 | 深兰科技（上海）有限公司 | A kind of method and apparatus of lane detection |
| CN110221603A (en) * | 2019-05-13 | 2019-09-10 | 浙江大学 | A kind of long-distance barrier object detecting method based on the fusion of laser radar multiframe point cloud |
| CN110297224A (en) * | 2019-08-01 | 2019-10-01 | 深圳前海达闼云端智能科技有限公司 | Laser radar positioning method and device, robot and computing equipment |
| CN110361010A (en) * | 2019-08-13 | 2019-10-22 | 中山大学 | It is a kind of based on occupy grating map and combine imu method for positioning mobile robot |
| CN110645998A (en) * | 2019-09-10 | 2020-01-03 | 上海交通大学 | Dynamic object-free map segmentation establishing method based on laser point cloud |
| WO2020026294A1 (en) * | 2018-07-30 | 2020-02-06 | 学校法人 千葉工業大学 | Map generation system and mobile object |
| US20200183011A1 (en) * | 2018-12-11 | 2020-06-11 | Shanghai Tusen Weilai Artificial Intelligence Technology Co., Ltd. | Method for creating occupancy grid map and processing apparatus |
| CN111325794A (en) * | 2020-02-23 | 2020-06-23 | 哈尔滨工业大学 | Visual simultaneous localization and map construction method based on depth convolution self-encoder |
| CN111553937A (en) * | 2020-04-23 | 2020-08-18 | 东软睿驰汽车技术（上海）有限公司 | Laser point cloud map construction method, device, equipment and system |
| CN111737395A (en) * | 2020-08-19 | 2020-10-02 | 浙江欣奕华智能科技有限公司 | Method and device for generating occupancy grid map and robot system |
| US20200326721A1 (en) * | 2020-06-26 | 2020-10-15 | Intel Corporation | Occupancy verification device and method |
| CN112100298A (en) * | 2020-08-17 | 2020-12-18 | 深圳市优必选科技股份有限公司 | Drawing establishing method and device, computer readable storage medium and robot |
| CN112313477A (en) * | 2018-06-22 | 2021-02-02 | 马瑞利欧洲公司 | Method for vehicle environment mapping and corresponding system, vehicle and computer program product |
| CN112578798A (en) * | 2020-12-18 | 2021-03-30 | 珠海格力智能装备有限公司 | Robot map acquisition method and device, processor and electronic device |
| CN112581612A (en) * | 2020-11-17 | 2021-03-30 | 上汽大众汽车有限公司 | Vehicle-mounted grid map generation method and system based on fusion of laser radar and look-around camera |
-
2021
- 2021-03-31 CN CN202110352201.8A patent/CN113093221A/en active Pending
Patent Citations (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101413806A (en) * | 2008-11-07 | 2009-04-22 | 湖南大学 | Mobile robot grating map creating method of real-time data fusion |
| KR20110080395A (en) * | 2010-01-05 | 2011-07-13 | 주식회사 코아로직 | Motion detection device and method, and image processing apparatus comprising the same device |
| DE102015201566A1 (en) * | 2015-01-29 | 2016-08-04 | Conti Temic Microelectronic Gmbh | PICTURE IDENTIFICATION, PICTURE IDENTIFICATION SYSTEM AND VEHICLE CONTROL SYSTEM |
| CN104865578A (en) * | 2015-05-12 | 2015-08-26 | 上海交通大学 | Indoor parking lot high-precision map generation device and method |
| KR20170001148A (en) * | 2015-06-25 | 2017-01-04 | 현대자동차주식회사 | System and Method for writing Occupancy Grid Map of sensor centered coordinate system using laser scanner |
| US20170278221A1 (en) * | 2016-03-22 | 2017-09-28 | Samsung Electronics Co., Ltd. | Method and apparatus of image representation and processing for dynamic vision sensor |
| CN112313477A (en) * | 2018-06-22 | 2021-02-02 | 马瑞利欧洲公司 | Method for vehicle environment mapping and corresponding system, vehicle and computer program product |
| CN112352272A (en) * | 2018-07-30 | 2021-02-09 | 千叶工业大学 | Map generation system and moving object |
| WO2020026294A1 (en) * | 2018-07-30 | 2020-02-06 | 学校法人 千葉工業大学 | Map generation system and mobile object |
| CN109186586A (en) * | 2018-08-23 | 2019-01-11 | 北京理工大学 | One kind towards dynamically park environment while position and mixing map constructing method |
| CN109085838A (en) * | 2018-09-05 | 2018-12-25 | 南京理工大学 | A kind of dynamic barrier rejecting algorithm based on laser positioning |
| US20200183011A1 (en) * | 2018-12-11 | 2020-06-11 | Shanghai Tusen Weilai Artificial Intelligence Technology Co., Ltd. | Method for creating occupancy grid map and processing apparatus |
| CN109829386A (en) * | 2019-01-04 | 2019-05-31 | 清华大学 | Intelligent vehicle based on Multi-source Information Fusion can traffic areas detection method |
| CN110008851A (en) * | 2019-03-15 | 2019-07-12 | 深兰科技（上海）有限公司 | A kind of method and apparatus of lane detection |
| CN110221603A (en) * | 2019-05-13 | 2019-09-10 | 浙江大学 | A kind of long-distance barrier object detecting method based on the fusion of laser radar multiframe point cloud |
| CN110297224A (en) * | 2019-08-01 | 2019-10-01 | 深圳前海达闼云端智能科技有限公司 | Laser radar positioning method and device, robot and computing equipment |
| CN110361010A (en) * | 2019-08-13 | 2019-10-22 | 中山大学 | It is a kind of based on occupy grating map and combine imu method for positioning mobile robot |
| CN110645998A (en) * | 2019-09-10 | 2020-01-03 | 上海交通大学 | Dynamic object-free map segmentation establishing method based on laser point cloud |
| CN111325794A (en) * | 2020-02-23 | 2020-06-23 | 哈尔滨工业大学 | Visual simultaneous localization and map construction method based on depth convolution self-encoder |
| CN111553937A (en) * | 2020-04-23 | 2020-08-18 | 东软睿驰汽车技术（上海）有限公司 | Laser point cloud map construction method, device, equipment and system |
| US20200326721A1 (en) * | 2020-06-26 | 2020-10-15 | Intel Corporation | Occupancy verification device and method |
| CN112100298A (en) * | 2020-08-17 | 2020-12-18 | 深圳市优必选科技股份有限公司 | Drawing establishing method and device, computer readable storage medium and robot |
| CN111737395A (en) * | 2020-08-19 | 2020-10-02 | 浙江欣奕华智能科技有限公司 | Method and device for generating occupancy grid map and robot system |
| CN112581612A (en) * | 2020-11-17 | 2021-03-30 | 上汽大众汽车有限公司 | Vehicle-mounted grid map generation method and system based on fusion of laser radar and look-around camera |
| CN112578798A (en) * | 2020-12-18 | 2021-03-30 | 珠海格力智能装备有限公司 | Robot map acquisition method and device, processor and electronic device |
Non-Patent Citations (8)
| Title |
|---|
| M. HSIAO, E. WESTMAN, G. ZHANG AND M. KAESS: "Keyframe-based dense planar SLAM", 《IEEE》, 29 May 2017 (2017-05-29), pages 5110 - 5117, XP033127339, DOI: 10.1109/ICRA.2017.7989597 * |
| S. PI, B. HE, S. ZHANG, R. NIAN, Y. SHEN AND T. YAN: "Stereo visual SLAM system in underwater environment", 《IEEE》, 24 November 2014 (2014-11-24), pages 1 - 5 * |
| 吴学易: "基于激光里程计的无人驾驶汽车位姿估计研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》, no. 1, 15 January 2019 (2019-01-15), pages 035 - 437 * |
| 段建民;郑凯华;周俊静;: "多层激光雷达在无人驾驶车中的环境感知", 《北京工业大学学报》, vol. 40, no. 12, 9 December 2014 (2014-12-09), pages 1891 - 1898 * |
| 王文斐: "面向室内动态环境的机器人定位与地图构建", 《中国优秀硕士学位论文全文数据库 信息科学辑》, no. 7, 15 July 2012 (2012-07-15), pages 140 - 68 * |
| 王晨捷;罗斌;李成源;王伟;尹露;赵青;: "无人机视觉SLAM协同建图与导航", 《测绘学报》, vol. 49, no. 06, 15 June 2020 (2020-06-15), pages 767 - 776 * |
| 王立玲, 苏华强, 马东: "基于ICP匹配和贝叶斯逆传感器模型的地图构建", 《激光杂志》, vol. 41, no. 12, 25 December 2020 (2020-12-25), pages 50 - 56 * |
| 秦玉鑫;张高峰;王裕清;: "针对复杂环境的模块化栅格地图构建算法", 《控制工程》, vol. 23, no. 10, 20 October 2016 (2016-10-20), pages 1627 - 1633 * |
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