Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board

Kim, Junha; Kim, Changhyeon; Han, Young‐Soo; Kim, H. Jin

doi:10.1109/icra48506.2021.9561175

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…We note that there are few studies on calibration of multiple LiDARs. [2] Although there exist methods in this topic, but they don't work for roadside multiple LiDARs because of their own limitations and low algorithmic performance. Most studies have focused on the calibration of the two sensors in space, and assumed that the time between sensors have been synchronized [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…[2] Although there exist methods in this topic, but they don't work for roadside multiple LiDARs because of their own limitations and low algorithmic performance. Most studies have focused on the calibration of the two sensors in space, and assumed that the time between sensors have been synchronized [2], [3], [4]. For the time dimension, [5] used Random sample consensus(RANSAC) algorithm to align the trajectories of the LiDARs, but they assumed at most one person can be seen.…”

Section: Introductionmentioning

confidence: 99%

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TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

Ren¹,

Zhang²,

Li³

et al. 2023

Preprint

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Recently, it has become popular to deploy sensors such as LiDARs on the roadside to monitor the passing traffic and assist autonomous vehicle perception. Unlike autonomous vehicle systems, roadside sensors are usually affiliated with different subsystems and lack synchronization both in time and space. Calibration is a key technology which allows the central server to fuse the data generated by different location infrastructures, which can deliver improve the sensing range and detection robustness. Unfortunately, existing calibration algorithms often assume that the LiDARs are significantly overlapped or that the temporal calibration is already achieved. Since these assumptions do not always hold in the real world, the calibration results from the existing algorithms are often unsatisfactory and always need human involvement, which brings high labor costs.In this paper, we propose TrajMatch -the first system that can automatically calibrate for roadside LiDARs in both time and space. The main idea is to automatically calibrate the sensors based on the result of the detection/tracking task instead of extracting special features. More deeply, we propose a mechanism for evaluating calibration parameters that is consistent with our algorithm, and we demonstrate the effectiveness of this scheme experimentally, which can also be used to guide parameter iterations for multiple calibration. Finally, to evaluate the performance of TrajMatch , we collect two dataset, one simulated dataset LiDARnet-sim 1.0 and a real-world dataset. Experiment results show that TrajMatch can achieve a spatial calibration error of less than 10cm and a temporal calibration error of less than 1.5ms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

Ren¹,

Zhang²,

Li³

et al. 2023

Preprint

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“…Inspired by LiDAR-Camera calibration method, some other types of calibration targets are designed. A plain planar board, attached with strong refection materials, is utilized for multi-LiDAR calibration in [34] and a user-friendly automated multi-LiDAR extrinsic calibration method is proposed only using LiDAR data.…”

Section: Offline Short Baseline Indoormentioning

confidence: 99%

“…Considering the challenges described above, current solutions are far from satisfactory. Some papers mainly focus on short baseline cases [11], [12], [13], [34], [36] and small viewpoint difference [26], [29], [30], [31], [32] as shown in Fig. 4.3 where two LiDARs are placed relatively close each other and are placed with small viewpoint difference.…”

Section: Proposed Solutionmentioning

confidence: 99%

Online extrinsic calibration of multiple long baseline ranging sensors

Qiao¹

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I wish to express my sincere appreciation to my advisor Prof. Wang Dan Wei for his guidance and continuous support throughout my master study for providing not only the lab resources but also mental care. Also I would like to express many thanks for Dr. Zhang Jun and Dr. Wen Mingxing for their meticulous guidance to mentor my research work. I am grateful for NTU for providing special support during this COVID-19 period. Furthermore, I would like to thank my parents and my dear friends who teach me how to stay positive and peaceful to cope with ups and downs in lifetime.

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“…the plane of a rectangular board [49], and the planes in the environment (wall, ground, etc.) [9] [50] [26].…”

Section: Categorization Of Multi-sensor Extrinsic Calibrationmentioning

confidence: 99%

Multi-sensor calibration and multi-modal perception for intelligent systems

Zhang¹

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First of all, I would like to express my special and great appreciation to my supervisor, Prof. Wang Danwei. Five years ago, Prof. Wang provided this precious opportunity for me to pursue a Ph.D. in Robotics even I was a freshman. During my Ph.D. study, Prof. Wang provided invaluable guidance and continuous support. His encouragement and praise have brought me through the difficulties and harsh times in my Ph.D. journey. This thesis would never be possible without his brilliant ideas and extraordinary insight on research directions. He is not only my Ph.D. supervisor, but also my life mentor. I am grateful to NTU and STE-NTU Corporate Lab for providing me with the financial support and lab resources to carry out my research work. Thanks to Yue

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Automated Extrinsic Calibration for 3D LiDARs with Range Offset Correction using an Arbitrary Planar Board

Cited by 10 publications

References 24 publications

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

TrajMatch: Towards Automatic Spatio-temporal Calibration for Roadside LiDARs through Trajectory Matching

Online extrinsic calibration of multiple long baseline ranging sensors

Multi-sensor calibration and multi-modal perception for intelligent systems

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