Undistorted raw LiDAR scans and static point extractions method for ground truth static scene sparse flow generation

1. A method of generating a ground truth dataset for motion planning for a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising:

transforming LiDAR scans into undistorted LiDAR scans based on LiDAR's poses for the motion planning of a vehicle;

identifying, for a pair of undistorted LiDAR scans, points belonging to a static object in an environment;

aligning the close points based on pose estimates; and

transforming a reference scan that is close in time to a target undistorted LiDAR scan so as to align the reference scan with the target undistorted LiDAR scan.

2. The method according to claim 1 further comprising:

determining that a distance between a point in the target undistorted LiDAR scan and its closest point in the aligned reference scan is smaller than a threshold.

3. The method according to claim 2 further comprising:

extracting the point from the target undistorted LiDAR scan.

4. The method according to claim 3 further comprising:

generating a LiDAR static-scene point cloud based on undistorted LiDAR scans.

5. The method according to claim 1 , further comprising:

collecting LiDAR scans.

6. The method according to claim 5 further comprising:

collecting LiDAR scans in the form of a swing while observing points within the swing at different time points.

7. The method according to claim 6 , wherein collecting the LiDAR scans further comprising:

collecting the LiDAR scans in the form of a swing in 20 Hz.

8. The method according to claim 5 further comprising:

detecting two pose estimates that are closest to LiDAR scan acquisition time.

9. The method according to claim 8 , wherein the pose estimates include GNSS-inertial pose estimates.

10. The method according to claim 8 further comprising:

determining LiDAR's poses based on interpolation.

11. The method according to claim 10 , wherein the interpolation includes a linear interpolation.

12. A system for generating a ground truth dataset for motion planning, the system comprising:

an internet server, comprising:

an I/O port, configured to transmit and receive electrical signals to and from a client device;

a memory;

one or more processing units; and

one or more programs stored in the memory and configured for execution by the one or more processing units, the one or more programs including instructions for:

a collecting module configured to collect LiDAR scans for the motion planning of a vehicle;

a detecting module configured to detect two pose estimates that are closest to LiDAR scan accusation time;

a determining module configured to determine LiDAR's poses by interpolating the two pose estimates; and

a transforming module configured to transform the LiDAR scans into undistorted LiDAR scans based on the LiDAR's poses.

13. The system according to claim 12 , wherein the collecting module is configured to collect LiDAR scans in the form of a swing while observing points within the swing at different time points.

14. The system according to claim 13 , wherein the collecting module is configured to collect LiDAR scans in the form of a swing in 20 Hz.

15. The system according to claim 12 , wherein the pose estimates include GNSS-inertial pose estimates.

16. The system according to claim 12 , wherein the interpolation includes a linear interpolation.

17. A system having a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions which, when executed by the one or more processors of a computing device, cause the computing device to:

collect LiDAR scans for a motion planning of a vehicle;

detect two pose estimates that are closest to LiDAR scan accusation time;

determine LiDAR's poses by interpolating the two pose estimates; and

transform the LiDAR scans into undistorted LiDAR scans based on the LiDAR's poses.

18. The system according to claim 17 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

identify, for a pair of undistorted LiDAR scans, points belonging to a static object in an environment.

19. The system according to claim 18 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

align the close points based on GNSS-inertial estimates.

20. The system according to claim 19 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

transform a reference scan that is close in time to a target undistorted LiDAR scan so as to align the reference scan with the target undistorted LiDAR scan.

21. The system according to claim 20 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

determine that a distance between a point in the target undistorted LiDAR scan and its closest point in the aligned reference scan is smaller than a threshold.

22. The system according to claim 21 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

extract the point from the target undistorted LiDAR scan.

23. The system according to claim 22 , further comprising instructions which, when executed by the one or more processors, cause the computing device to:

generate a LiDAR static-scene point cloud based on undistorted LiDAR scans.

24. A method of generating a ground truth dataset for motion planning for a non-transitory computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising:

collecting LiDAR scans for the motion planning of a vehicle;

detecting two pose estimates that are closest to LiDAR scan accusation time;

determining LiDAR's poses by linearly interpolating the two GNSS-inertial pose estimates that are closest to LiDAR scan accusation time; and

transforming the LiDAR scans into undistorted LiDAR scans based on the LiDAR's poses.

25. The method according to claim 24 , wherein collecting LiDAR scans further comprises:

collecting the LiDAR scans in the form of a swing while observing points within the swing at different time points.

26. The method according to claim 25 further comprising:

collecting LiDAR scans in the form of a swing in 20 Hz.

27. The method according to claim 24 , wherein the interpolation includes a linear interpolation.

28. The method according to claim 24 , further comprising:

identifying, for a pair of undistorted LiDAR scans, points belonging to a static object in an environment.

29. The method according to claim 28 , further comprising:

aligning close points based on GNSS-inertial estimates.

30. The method according to claim 29 , further comprising:

transforming a reference scan that is close in time to a target undistorted LiDAR scan so as to align the reference scan with the target undistorted LiDAR scan.

31. The method according to claim 30 , further comprising:

determining that a distance between a point in the target undistorted LiDAR scan and its closest point in the aligned reference scan is smaller than a threshold.

32. The method according to claim 31 , further comprising:

extracting the point from the target undistorted LiDAR scan.

33. The method according to claim 32 , further comprising:

generating a LiDAR static-scene point cloud based on the undistorted LiDAR scans.