CN108240807B - Method for estimating space occupation - Google Patents
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- CN108240807B CN108240807B CN201611226316.8A CN201611226316A CN108240807B CN 108240807 B CN108240807 B CN 108240807B CN 201611226316 A CN201611226316 A CN 201611226316A CN 108240807 B CN108240807 B CN 108240807B
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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
- G01C21/005—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 with correlation of navigation data from several sources, e.g. map or contour matching
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The embodiment of the invention provides a method for estimating space occupation, which belongs to the field of automatic driving and comprises the following steps: calculating filtering information of each point in the point cloud information detected by the sensor under a map coordinate system, wherein the map coordinate system is fixed relative to the position of the sensor, and gesture information in the point cloud information detected by the sensor is consistent in the map coordinate system and a world coordinate system; the occupancy probability of the space grid is calculated according to the filtering information of each point in the space grid in the point cloud information. According to the embodiment of the invention, the space occupation condition can be estimated by utilizing the data in the map coordinate system which is relatively fixed with the sensor, so that the introduced error is reduced, the estimation accuracy is further improved, and the estimation deviation is reduced.
Description
Technical Field
The present invention relates to the field of autopilot, and in particular to a method of estimating space occupation.
Background
In automatic driving, it is necessary to use a sensor to detect a space object and obtain point cloud data, and then estimate the occupation of the space from the point cloud data. For example, when driving automatically on bumpy roads in the field, if the GPS signal is not stable, the point cloud information can be used to estimate the space occupancy to provide a priori information for path planning and object recognition.
At present, the estimation of space occupation is to convert point cloud data under a sensor coordinate system into data under a static world coordinate system, and estimate the space occupation according to the data under the world coordinate system. The inventor of the application finds that the scheme in the prior art has the defects of low accuracy and overlarge deviation of space occupation estimation.
Disclosure of Invention
An object of an embodiment of the present invention is to provide a method for estimating space occupation, so as to solve the above technical problems or at least partially solve the above technical problems.
To achieve the above object, an embodiment of the present invention provides a method of estimating space occupation, including: calculating filtering information of each point in the point cloud information detected by the sensor under a map coordinate system, wherein the map coordinate system is fixed relative to the position of the sensor, and gesture information in the point cloud information detected by the sensor is consistent in the map coordinate system and a world coordinate system; the occupancy probability of the space grid is calculated according to the filtering information of each point in the space grid in the point cloud information.
Optionally, the calculating the filtering information of each point in the point cloud information detected by the sensor under the map coordinate system includes: converting position information of each point in point cloud information under a sensor coordinate system into position information under a map coordinate system, wherein the sensor coordinate system is a coordinate system established according to the sensor position; for each point in the point cloud information, calculating a corresponding measurement error and a corresponding posture error of the point according to the position information of the point under a map coordinate system; and calculating the filtering information of each point in the point cloud information according to the measurement error and the posture error of the point.
Optionally, the calculating the occupancy probability of the space grid according to the filtering information of each point located in the space grid in the point cloud information includes: for each point in the space grid in the point cloud information, calculating the occupation probability of the space grid corresponding to the point according to the position of the point; and calculating the occupation probability of the space grid by using the filtering information of each point in the space grid in the point cloud information and the occupation probability of the space grid corresponding to each point.
Optionally, for each point in the space grid in the point cloud information, calculating the occupation probability of the space grid corresponding to the point according to the position of the point includes: and respectively calculating the reciprocal of the distance between each point and the center of the space grid for each point positioned in the space grid in the point cloud information, and normalizing the calculated reciprocal of the distance to obtain the occupation probability of the space grid corresponding to the point.
Optionally, the calculating the occupation probability of the space grid by using the filtering information of each point located in the space grid in the point cloud information and the occupation probability of the space grid corresponding to each point includes: and iteratively updating the occupation probability of the space grid by utilizing the filtering information of the point positioned in the space grid in the point cloud information and the occupation probability of the space grid corresponding to the point.
According to another aspect of the present invention, there is provided an apparatus for estimating space occupation, the apparatus comprising: the filtering information calculation module is used for calculating the filtering information of each point in the point cloud information detected by the sensor under a map coordinate system, wherein the map coordinate system is fixed relative to the position of the sensor, and the posture information in the point cloud information detected by the sensor is consistent in the map coordinate system and the world coordinate system; and the occupation probability calculation module is used for calculating the occupation probability of the space grid according to the filtering information of each point positioned in the space grid in the point cloud information.
Optionally, the filtering information calculating module includes: the conversion unit is used for converting the position information of each point in the point cloud information under the sensor coordinate system into the position information under the map coordinate system, wherein the sensor coordinate system is a coordinate system established according to the sensor position; the first calculation unit is used for calculating corresponding measurement errors and posture errors of each point in the point cloud information according to the position information of the point under the map coordinate system; and the second calculation unit is used for calculating the filtering information of each point in the point cloud information according to the measurement error and the posture error of the point.
Optionally, the occupation probability calculation module includes: a third calculation unit, configured to calculate, for each point located in the spatial grid in the point cloud information, an occupancy probability of the spatial grid corresponding to the point according to a position of the point; and a fourth calculation unit for calculating the occupation probability of the space grid by using the filtering information of each point in the space grid in the point cloud information and the occupation probability of the space grid corresponding to each point.
Optionally, the third calculation unit is configured to calculate, for each point located in the space grid in the point cloud information, an inverse of a distance between the point and a center of the space grid, and normalize the calculated inverse of the distance to obtain an occupancy probability of the space grid corresponding to the point.
Optionally, the fourth calculating unit is configured to iteratively update the occupation probability of the spatial grid by using the filtering information of the point located in the spatial grid in the point cloud information and the occupation probability of the spatial grid corresponding to the point.
According to the technical scheme, the filtering information of each point in the point cloud information detected by the sensor is calculated under the map coordinate system, wherein the map coordinate system is fixed relative to the position of the sensor, and the posture information in the point cloud information detected by the sensor is consistent with the map coordinate system and the world coordinate system; calculating the occupation probability of the space grid according to the filtering information of each point in the space grid in the point cloud information; therefore, the space occupation condition can be estimated by utilizing the data in the map coordinate system which is relatively fixed with the sensor, the introduced error is reduced, the estimation accuracy is further improved, and the estimation deviation is reduced.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a flow chart of a method of estimating space occupation in accordance with an embodiment of the present invention;
FIG. 2 is a schematic illustration of a scenario in which a mobile robotic system travels in a bumpy environment in accordance with an embodiment of the present invention;
FIG. 3 is a flow chart of a process of calculating filter information according to an embodiment of the present invention;
FIG. 4 is a flow chart of a process for calculating a space grid occupancy probability based on filtering information of points according to an embodiment of the invention;
FIG. 5 is a flow chart of a method of estimating space occupancy in the scenario shown in FIG. 2, according to an embodiment of the invention;
FIG. 6 is a block diagram of an apparatus for estimating space occupation according to an embodiment of the present invention;
FIG. 7 is a block diagram of an apparatus for estimating space occupation according to an embodiment of the present invention; and
fig. 8 is a block diagram of an apparatus for estimating space occupation according to an embodiment of the present invention.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
Example 1
Fig. 1 is a flow chart of a method of estimating space occupation, which may be used in an autopilot system, e.g., a robot, an autopilot system of a vehicle, as shown in fig. 1, according to an embodiment of the present invention, which may include the following steps.
In step S110, filter information of each point in the point cloud information detected by the sensor is calculated under the map coordinate system.
The map coordinate system is fixed relative to the sensor, and the gesture information of the point cloud information detected by the sensor is consistent with the map coordinate system and the world coordinate system.
For example, in the environment shown in FIG. 2, the S point represents the sensor, the sensor coordinate system is at the origin of the S point, the stationary world coordinate system is at the origin of the I point, r IS Is the vector from point I to point S. The M point is fixed with respect to the S point, the map coordinate system takes the M point as an origin, and the posture information in the point cloud information is consistent in the map coordinate system and the world coordinate system, that is, the values of roll (roll), pitch (pitch), and yaw (rotation) are consistent in the map coordinate system and the world coordinate system.
In general, the point cloud information detected by the sensor is data information in a sensor coordinate system, the point cloud information in the sensor coordinate system is converted into data information in a map coordinate system, and then the filtering information of each point in the point cloud information is obtained according to the data information in the map coordinate system.
In step S120, the occupancy probability of the spatial grid is calculated from the filter information of each point located in the spatial grid in the point cloud information.
For example, the space is divided into cubes of a preset size, which are space grids, and the occupation probability of the space grids is calculated from the filter information of each point located in the space grids for each space grid based on the point cloud information, whereby the occupation situation of the space is obtained.
By the embodiment, the space occupation can be estimated by utilizing the data in the map coordinate system which is relatively fixed with the sensor, and only the attitude error and the measurement error are introduced, so that the introduced error amount is reduced, the estimation accuracy is improved, and the estimation deviation is reduced.
Example two
Fig. 3 is a flowchart of a process of calculating filtering information according to an embodiment of the present invention, and as shown in fig. 3, the calculating of the filtering information of each point in the point cloud information detected by the sensor in the map coordinate system may include the following steps.
In step S302, position information of each point in the point cloud information in the sensor coordinate system is converted into position information in the map coordinate system.
The sensor coordinate system is a coordinate system established according to the sensor position.
For example, in the environment shown in fig. 2, the position information of the point cloud information point P in the map coordinate system is shown in formula 1.
P m ＝mr MP ＝mr SP -mr SM ＝Cms(q|t)×sr SP -mr SM Equation 1
Wherein mr is MP Mr is the position of the P point in the map coordinate system SP In a map coordinate system S->Vector information of P (S point to P point), mr SM In a map coordinate system S->Vector information of M (S point to M point); cms is conversion information of a sensor coordinate system relative to a map coordinate system, q is a quaternion corresponding to coordinate conversion at time t, sr SP Is S->Vector information of P (S point to P point).
In step S304, for each point in the point cloud information, a measurement error and a posture error corresponding to the point are calculated from the position information of the point in the map coordinate system.
For example, mr in equation 1 is calculated because the origin of the map coordinate system and the origin of the sensor coordinate system are relatively fixed SM Is constant, so the error information comes from sr SP And q, corresponding to the measurement error and the attitude error, respectively. As shown in formula 2, for P m Deviation determination guide。
Ds＝dP m /d(sr SP )＝Cms(q|t)；Dq＝dP m /(dq)＝Cms×(sr SP x) equation 2
Wherein Ds is the partial guide of the measurement information, for example, the partial guide corresponding to the laser measurement information, representing the measurement error; dq is the partial derivative of the quaternion, representing the attitude error, sr SP x is sr SP Is a diagonal matrix of the matrix.
In step S306, for each point in the point cloud information, the filter information of the point is calculated from the measurement error and the posture error of the point.
For example, the filtering information of the points is calculated as in the following equation 3.
E＝Ds×Es×(Ds) T +Dq×Eq×(Dq) T Equation 3
Wherein E is filtering information of points, es is covariance matrix of estimated measurement errors of the laser sensor, matrix parameters are related to the sensor model, and Eq is covariance matrix of estimated measurement errors of the inertial navigation sensor. (Ds) T Transpose of Ds, (Dq) T Representing the transpose of Dq. This equation 3 is for an example using a laser sensor and a conventional navigation sensor, and any way that a person skilled in the art deems appropriate may be applied in different scenarios to obtain the filtered information of the points in the point cloud information.
According to the embodiment, the filtering information of each point in the point cloud information can be obtained, and the filtering information of the point is calculated by using the data under the map coordinate system, so that the calculation complexity is reduced, and the calculation efficiency is improved.
Example III
Fig. 4 is a flowchart of a process of calculating a space grid occupancy probability according to filtering information of points according to an embodiment of the present invention, and as shown in fig. 4, the calculating of the space grid occupancy probability according to filtering information of each point located in the space grid in the point cloud information may include the following steps.
In step S402, for each point in the point cloud information located in the space grid, the occupation probability of the space grid corresponding to the point is calculated from the position of the point.
Further, for each point in the space grid in the point cloud information, calculating the occupation probability of the space grid corresponding to the point according to the position of the point may include: respectively calculating the reciprocal of the distance between each point located in the space grid and the center of the space grid for each point located in the space grid in the point cloud information; normalizing the inverse of the calculated distance obtains the occupancy probability of the spatial grid corresponding to the point.
For example, for a point located in a space grid in cloud information, position information of the point is obtained from the point cloud information, a distance d between the point and a midpoint of the space grid is calculated by using the position information, 1/d of the reciprocal of d is taken, and then the 1/d is normalized to a value between 0 and 1 by using a normalization function. The means used for normalization may be any means deemed appropriate by the person skilled in the art.
In step S404, the occupation probability of the space grid is calculated using the filter information of each point located in the space grid in the point cloud information and the occupation probabilities of the space grid corresponding to each point.
Further, the calculating the occupancy probability of the spatial grid using the filtering information of each point located in the spatial grid in the point cloud information and the occupancy probability of the spatial grid corresponding to each point may include: the occupation probability of the space grid is iteratively updated with the filtering information of the point located in the space grid in the point cloud information and the occupation probability of the space grid corresponding to the point.
For example, initially, filter information of a point located in a space grid in the point cloud information and an occupation probability of the space grid for the point are stored as initial variance information and occupation probability of the space grid, respectively. Then, the filter information of another point located in the space grid and the occupation probability of the space grid for the point are taken to update the stored variance information and occupation probability of the space grid, so that the filter information of the point located in the space grid and the occupation probability of the space grid for the point are used one by one, and the stored occupation probability and the stored variance information of the space grid are iteratively updated until the point located in the space grid in the point cloud information is used. For example, the occupancy probability and variance information of the stored space grid are updated according to the following formulas 4 and 5, and finally the occupancy probability of the space grid in practice is estimated.
p+ = ((E) × (p) + (E-) × (p-)/((E-) +e) equation 4
E+ = (E-) × (E)/((E-) × (E)) formula 5
Wherein (p-, E-) represents the occupancy probability and variance information of the currently stored space grid, (p, E) represents the occupancy probability of the space grid corresponding to the point in the extracted point cloud information and the filtering information of the point, and (p+, E+) represents the occupancy probability and variance information of the updated space grid.
The above-described updating manner is not limited to the manner shown in the formulas 4 and 5, and may be performed using any manner deemed appropriate by those skilled in the art.
The embodiment can calculate the occupation probability of the space grid by using the related information of the points in the space grid in the point cloud information, and improves the accuracy of the occupation probability.
Example IV
Fig. 5 is a flowchart of a method of estimating space occupancy in the scenario shown in fig. 2, in which the present invention is illustrated by way of example to provide estimated space occupancy information for a moving robot, according to an embodiment of the present invention. As shown in fig. 5, the method may include the following steps. In step S502, position information of each point in the point cloud information in the sensor coordinate system is converted into position information in the map coordinate system. The map coordinate system takes an M point which is relatively fixed with an S point as an origin, wherein the gesture information in the point cloud information detected by the sensor at the S point is consistent in the map coordinate system and the world coordinate system. For example, the conversion may be performed as shown in equation 1. In step S504, for each point in the point cloud information, a measurement error and a posture error corresponding to the point are calculated from the position information of the point in the map coordinate system. For example, the measurement error and the posture error corresponding to each point in the point cloud information are calculated by the bias derivative method according to the formula 2. In step S506, for each point in the point cloud information, the filter information of the point is calculated from the measurement error and the posture error of the point. For example, the filtering information of points in the point cloud information is calculated in the manner shown in formula 3. In step S508, for each point in the point cloud information located in the space grid, the inverse of the distance between the point and the center of the space grid is calculated, and the calculated inverse of the distance is normalized to obtain the occupancy probability of the space grid corresponding to the point. In step S510, filter information of one point located in the space grid in the point cloud information and the occupation probability of the space grid for the point are stored as initial variance information and occupation probability of the space grid, respectively. In step S512, the filter information of the points located in the spatial grid and the occupation probability of the spatial grid for the points in the point cloud information are extracted one by one to iteratively update the stored variance information and occupation probability of the spatial grid until the points located in the spatial grid in the point cloud information are all used. For example, the occupancy probability and variance information of the stored space grid are updated in the manner of equation 4 and equation 5. Thus, the occupation probability of each space grid is obtained through the technical scheme, and the occupation situation of the space is estimated.
The technical solution in this embodiment is intended to illustrate the technical solution in the present invention, and is not intended to limit the protection scope of the present invention.
Fig. 6 is a block diagram of an apparatus for estimating space occupation, which may be used in an automatic driving system, e.g., a robot, an automatic driving system of a vehicle, according to an embodiment of the present invention, and which may include the following modules, as shown in fig. 6.
The filtering information calculating module 610 is configured to calculate filtering information of each point in the point cloud information detected by the sensor under the map coordinate system; the map coordinate system is fixed relative to the sensor, and gesture information in the point cloud information detected by the sensor is consistent in the map coordinate system and the world coordinate system;
the occupation probability calculation module 620 is configured to calculate an occupation probability of the spatial grid according to the filtering information of each point located in the spatial grid in the point cloud information.
By the embodiment, the space occupation can be estimated by utilizing the data in the map coordinate system which is relatively fixed with the sensor, and only the attitude error and the measurement error are introduced, so that the introduced error amount is reduced, the estimation accuracy is improved, and the estimation deviation is reduced.
In an alternative embodiment, as shown in fig. 7, the filtering information calculating module 610 may include the following units.
A conversion unit 702, configured to convert position information of each point in the point cloud information under a sensor coordinate system into position information under a map coordinate system, where the sensor coordinate system is a coordinate system established according to a sensor position;
a first calculating unit 704, configured to calculate, for each point in the point cloud information, a measurement error and a posture error corresponding to the point according to position information of the point in a map coordinate system;
the second calculating unit 706 is configured to calculate, for each point in the point cloud information, filter information of the point according to a measurement error and a posture error of the point.
According to the embodiment, the filtering information of each point in the point cloud information can be obtained, and the filtering information of the point is calculated by using the data under the map coordinate system, so that the calculation complexity is reduced, and the calculation efficiency is improved.
In an alternative embodiment, as shown in FIG. 8, the occupancy probability calculation module 620 may include the following.
The third calculating unit 802 is configured to calculate, for each point located in the spatial grid in the point cloud information, an occupancy probability of the spatial grid corresponding to the point according to the position of the point.
Further, the third calculating unit 802 is configured to calculate, for each point located in the space grid in the point cloud information, an inverse of a distance between the point and a center of the space grid, and normalize the calculated inverse of the distance to obtain an occupancy probability of the space grid corresponding to the point.
A fourth calculation unit 804 for calculating an occupation probability of the space grid using the filter information of each point located in the space grid in the point cloud information and the occupation probability of the space grid corresponding to each point.
Further, the fourth calculating unit 804 is configured to iteratively update the occupation probability of the spatial grid using the filtering information of the point located in the spatial grid in the point cloud information and the occupation probability of the spatial grid corresponding to the point.
The embodiment can calculate the occupation probability of the space grid by using the related information of the points in the space grid in the point cloud information, and improves the accuracy of the occupation probability.
The above apparatus corresponds to the above method, and the detailed description of the specific embodiment will be referred to in the above method, which is not repeated here.
The foregoing details of the optional implementation manner of the embodiment of the present invention have been described above with reference to the accompanying drawings, but the embodiment of the present invention is not limited to the specific details of the foregoing implementation manner, and various simple modifications may be made to the technical solution of the embodiment of the present invention within the scope of the technical concept of the embodiment of the present invention, and these simple modifications all fall within the protection scope of the embodiment of the present invention.
In addition, the specific features described in the above embodiments may be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, various possible combinations of embodiments of the present invention are not described in detail.
Those skilled in the art will appreciate that all or part of the steps in implementing the methods of the embodiments described above may be implemented by a program stored in a storage medium, including instructions for causing a (which may be a single-chip microcomputer, a chip or the like) or processor (processor) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In addition, any combination of various embodiments of the present invention may be performed, so long as the concept of the embodiments of the present invention is not violated, and the disclosure of the embodiments of the present invention should also be considered.
Claims (4)
1. A method of estimating space occupation, the method comprising:
calculating variance information of each point in the point cloud information detected by the sensor under a map coordinate system, wherein the map coordinate system is fixed relative to the sensor, and gesture information in the point cloud information detected by the sensor is consistent in the map coordinate system and a world coordinate system;
the variance information of each point in the point cloud information is calculated by the following formula:
E＝Ds×Es×(Ds) T +Dq×Eq×(Dq) T
wherein E is the variance information of the point, es is the covariance matrix of the estimated measurement error of the laser sensor, matrix parameters are related to the sensor model, and Eq is the covariance matrix of the estimated measurement error of the inertial navigation sensor; ds is the measurement error of each point in the point cloud information, (Ds) T The transpose of Ds, dq is the attitude error of each point in the point cloud information, (Dq) T Representing the transposition of the Dq, and calculating the occupation probability of the space grid according to the variance information of each point positioned in the space grid in the point cloud information;
taking the variance information of a point in the space grid in the point cloud information and the occupation probability of the space grid for the point as initial variance information and occupation probability of the space grid respectively to store;
extracting variance information of points in the space grid and occupation probability of the space grid for the points one by one in the point cloud information, and iteratively updating the stored variance information and occupation probability of the space grid until the points in the space grid are used in the point cloud information;
the occupation probability and variance information of the stored space grid are updated by the following formula, and finally the occupation probability of the space grid in reality is estimated:
p+＝((E)×(p)+(E-)×(p-))/((E-)+E)
E+＝(E-)×(E)/((E-)×(E))
wherein (p-, E-) represents the occupancy probability and variance information of the currently stored space grid, (p, E) represents the occupancy probability and variance information of the space grid corresponding to the point in the extracted point cloud information, and (p+, E+) represents the occupancy probability and variance information of the updated space grid.
2. The method of claim 1, wherein the step of determining the position of the substrate comprises,
the calculating the variance information of each point in the point cloud information detected by the sensor under the map coordinate system comprises the following steps:
converting position information of each point in point cloud information under a sensor coordinate system into position information under a map coordinate system, wherein the sensor coordinate system is a coordinate system established according to the sensor position;
for each point in the point cloud information, calculating a corresponding measurement error and a corresponding posture error of the point according to the position information of the point under a map coordinate system;
and calculating variance information of each point in the point cloud information according to the measurement error and the posture error of the point.
3. The method of claim 1, wherein calculating the occupancy probability of the spatial grid based on variance information of points located in the spatial grid in the point cloud information comprises:
for each point in the space grid in the point cloud information, calculating the occupation probability of the space grid corresponding to the point according to the position of the point;
and calculating the occupation probability of the space grid by using the variance information of each point positioned in the space grid in the point cloud information and the occupation probability of the space grid corresponding to each point.
4. A method according to claim 3, wherein for each point in the point cloud information that is located in the spatial grid, calculating the occupancy probability of the spatial grid for that point based on the location of that point comprises:
and respectively calculating the reciprocal of the distance between each point and the center of the space grid for each point positioned in the space grid in the point cloud information, and normalizing the calculated reciprocal of the distance to obtain the occupation probability of the space grid corresponding to the point.
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