A Calibration Method of 2D Lidar and a Camera Based on Effective Lower Bound Estimation of Observation Probability

PENG Meng; WAN Qin; CHEN Baifan; WU Shuyue

doi:10.11999/JEIT210800

Volume 44 Issue 7

Jul. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(7): 2478-2487

PENG Meng, WAN Qin, CHEN Baifan, WU Shuyue. A Calibration Method of 2D Lidar and a Camera Based on Effective Lower Bound Estimation of Observation Probability[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2478-2487. doi: 10.11999/JEIT210800

Citation:

PENG Meng, WAN Qin, CHEN Baifan, WU Shuyue. A Calibration Method of 2D Lidar and a Camera Based on Effective Lower Bound Estimation of Observation Probability[J]. Journal of Electronics & Information Technology, 2022, 44(7): 2478-2487. doi: 10.11999/JEIT210800

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A Calibration Method of 2D Lidar and a Camera Based on Effective Lower Bound Estimation of Observation Probability

doi: 10.11999/JEIT210800

1.
School of Computer and Communication, Hunan Institute Of Engineering, Xiangtan 411104, China
2.
School of Automation, Central South University, Changsha 410083, China

Funds: The National Natural Science Foundation of China (62173134, 62006075), The Natural Science Foundation of Hunan Province (2021JJ10002, 2020JJ4246), The Hunan Education Department Funded Research Project (18B386, 18A356), The Innovation and Entrepreneurship Training Program for College Students in Hunan Province (S201911342021)

Received Date: 2021-08-09
Rev Recd Date: 2022-04-01

Available Online: 2022-04-14

Publish Date: 2022-07-25

Abstract

Abstract

Considering the multi solution problem of minimum solution method for calibration of 2D lidar and camera, a calibration method based on the estimation of the effective lower bound of observation probability is proposed. Firstly, a hierarchical clustering method with minimum solution set is proposed which should be used to replace the original solution set with each kind of optimal solution, so as to reduce the number of samples in the solution set. Then, a joint observation probability measure based on laser error is proposed to measure the quality of solutions. Finally, using the clustering results and the measurement results of observation probability, an effective solution selection strategy based on the estimation of the effective lower bound of observation probability is proposed, which transforms the optimized initial value from the optimal solution to the candidate set of effective solutions, and improves the accuracy of calibration results. Comparing with the existing methods, results of both simulation and real data experiment show that the proposed algorithm improves significantly the true solution hit rate by 16%～20% under different number of checkerboards and 6%～20% under different noise levels.
- Extrinsic calibration,
- 2D lidar,
- Camera,
- Multi solution problem,
- Effective lower bound estimation

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References(18)

References

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