Adaptive Point-Line Fusion: A Targetless LiDAR–Camera Calibration Method with Scheme Selection for Autonomous Driving

Author:

Zhou Yingtong1,Han Tiansi2,Nie Qiong2,Zhu Yuxuan3,Li Minghu4,Bian Ning4,Li Zhiheng1

Affiliation:

1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China

2. Meituan, Lizedong Road, Chaoyang District, Beijing 100102, China

3. Department of Automation, Tsinghua University, Beijing 100084, China

4. Dongfeng Motor Group Co., Ltd., Wuhan 430058, China

Abstract

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR–camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

Funder

National Key R&D Program of China

Publisher

MDPI AG

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3. Mishra, S., Pandey, G., and Saripalli, S. (November, January 19). Extrinsic Calibration of a 3D-LIDAR and a Camera. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.

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5. Chien, H.-J., Klette, R., Schneider, N., and Franke, U. (2016, January 4–8). Visual Odometry Driven Online Calibration for Monocular Lidar-Camera Systems. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.

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