TY - GEN
T1 - Basic Experiment of LIDAR Sensor Measurement Directional Instability for Moving and Vibrating Object
AU - Asano, Toshiki
AU - Toda, Hideki
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, a basic study on directional instability of measured data in a vehicle equipped with a LIDAR sensor. To realize autonomous car control without human, it is important to estimate the correct current position and it would be measured by LIDAR device generally. The difficulty in the process is the angle estimation. In rotational motion, all points in the point cloud data shift to different distances and directions, making the estimation unstable.We improved ICP to remove the instability of angle estimation. One is the two-sided search method, which improves the way points correspond in ICP. The other one is to convert the shape data (point cloud data) to the r-6 coordinate system so that the point cloud can easily obtain the best correspondence. We conducted estimation using point cloud data that imitate a road with fences placed on both sides. As a result, the range of accuracy within 0.1 degrees of the estimation error for the twosided search method was 0.63 times larger than that for ICP. Similarly, in the case of ICP in the r-6 coordinate system, the result was 1.76 times larger. Similarly, in the case of ICP in the r6 coordinate system, the result was 1.76 times larger. An analysis of the evaluation function E revealed the reason why the twosided search method did not estimate well. It was because each point prevented the selection of the correct correspondence.These results indicate that ease of response is important in self-location estimation. However, the r-6 coordinate system is not suitable for estimating movement, we would like to improve it so that it can be used in all situations.
AB - In this paper, a basic study on directional instability of measured data in a vehicle equipped with a LIDAR sensor. To realize autonomous car control without human, it is important to estimate the correct current position and it would be measured by LIDAR device generally. The difficulty in the process is the angle estimation. In rotational motion, all points in the point cloud data shift to different distances and directions, making the estimation unstable.We improved ICP to remove the instability of angle estimation. One is the two-sided search method, which improves the way points correspond in ICP. The other one is to convert the shape data (point cloud data) to the r-6 coordinate system so that the point cloud can easily obtain the best correspondence. We conducted estimation using point cloud data that imitate a road with fences placed on both sides. As a result, the range of accuracy within 0.1 degrees of the estimation error for the twosided search method was 0.63 times larger than that for ICP. Similarly, in the case of ICP in the r-6 coordinate system, the result was 1.76 times larger. Similarly, in the case of ICP in the r6 coordinate system, the result was 1.76 times larger. An analysis of the evaluation function E revealed the reason why the twosided search method did not estimate well. It was because each point prevented the selection of the correct correspondence.These results indicate that ease of response is important in self-location estimation. However, the r-6 coordinate system is not suitable for estimating movement, we would like to improve it so that it can be used in all situations.
KW - ICP
KW - LIDAR
KW - self position estimation
UR - http://www.scopus.com/inward/record.url?scp=85156163496&partnerID=8YFLogxK
U2 - 10.1109/RAAI56146.2022.10092994
DO - 10.1109/RAAI56146.2022.10092994
M3 - 会議への寄与
AN - SCOPUS:85156163496
T3 - 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022
SP - 148
EP - 151
BT - 2022 2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Robotics, Automation and Artificial Intelligence, RAAI 2022
Y2 - 9 December 2022 through 11 December 2022
ER -