A fast and accurate camera-IMU calibration method for localization system

Xiaowen Tao; Pengxiang Meng; Bing Zhu; Jian Zhao

doi:10.18282/iac.v6i1.578

Xiaowen Tao Jilin University
Pengxiang Meng Jilin University
Bing Zhu Jilin University
Jian Zhao Jilin University

Ariticle ID: 578

352 Views, 106 PDF Downloads

DOI: https://doi.org/10.18282/iac.v6i1.578

Keywords: camera-IMU calibration, autonomous vehicles, sensor fusion, slam, localization system

Abstract

Autonomous driving has spurred the development of sensor fusion techniques, which combine data from multiple sensors to improve system performance. In particular, a localization system based on sensor fusion, such as Visual Simultaneous Localization and Mapping (VSLAM), plays a crucial role in environment perception and serves as the foundation for decision-making and motion control in intelligent vehicles. The accuracy of extrinsic calibration parameters between the camera and IMU is of utmost importance for precise positioning in VSLAM systems. However, existing calibration methods are often time-consuming, rely on complex optimization techniques, and are sensitive to noise and outliers, leading to potential degradation in system performance. To address these challenges, this paper presents a fast and accurate camera-IMU calibration method based on space coordinate transformation constraints and SVD (Singular Value Decomposition) tricks. The method involves constructing constraint equations by ensuring the equality of rotation and transformation matrices between camera frames and IMU coordinates at different time instances. Subsequently, the external parameters of the camera-IMU system are solved using quaternion transformation and SVD techniques. To validate the proposed method, experiments were conducted using the ROS (Robot Operating System) platform, where camera images and velocity, acceleration, and angular velocity data from the IMU were recorded in a ROS bag file. The results demonstrate that the proposed method achieves reliable camera-IMU calibration parameters, requiring less tuning time and exhibiting reduced uncertainty.

References

Zhu B, Tao X, Zhao J, et al. An integrated GNSS/UWB/DR/VMM positioning strategy for intelligent vehicles. IEEE Transactions on Vehicular Technology 2020; 69(10): 10842–10853. doi: 10.1109/TVT.2020.3014516

Zhu B, Zhang P, Zhao J, et al. Hazardous scenario enhanced generation for automated vehicle testing based on optimization searching method. IEEE Transactions on Intelligent Transportation Systems 2021; 23(7): 7321–7331. doi: 10.1109/TITS.2021.3068784

Li X, Tao X, Zhu B, et al. Research on a simulation method of the millimeter wave radar virtual test environment for intelligent driving. Sensors 2020; 20(7): 1929. doi: 10.3390/s20071929

Tao X, Zhu B, Xuan S, et al. A multi-sensor fusion positioning strategy for intelligent vehicles using global pose graph optimization. IEEE Transactions on Vehicular Technology 2021; 71(3): 2614–2627. doi: 10.1109/TVT.2021.3139006

Zhu B, Tao X, Zhao J, et al. Two-stage UWB positioning algorithm of intelligent vehicle. Journal of Traffic Engineering 2021; 21(2): 256–266. doi: 10.19818/j.cnki.1671-1637.2021.02.022

Zhang Z. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(11): 1330–1334. doi: 10.1109/34.888718

Heikkila J, Silvén O. A four-step camera calibration procedure with implicit image correction. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 17–19 June 1997; San Juan, USA. pp. 1106–1112.

Salvi J, Armangué X, Batlle J. A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recognition 2002; 35(7): 1617–1635. doi: 10.1016/S0031-3203(01)00126-1

Zhang Z, Scaramuzza D. A tutorial on quantitative trajectory evaluation for visual (-inertial) odometry. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 1–5 October 2018; Madrid, Spain. pp. 7244–7251.

Brink K, Soloviev A. Filter-based calibration for an IMU and multi-camera system. In: Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium; 23–26 April 2012; Myrtle Beach, USA. pp. 730–739.

Sünderhauf N. An Outlook on Robust Optimization for Sensor Fusion and Calibration: Switchable Constraints for Robust Simultaneous Localization and Mapping and Satellite-Based Localization. Springer International Publishing; 2023. pp. 167–170.

Zhao J, Li Y, Zhu B, et al. Method and applications of LiDAR modeling for virtual testing of intelligent vehicles. IEEE Transactions on Intelligent Transportation Systems 2020; 22(5): 2990–3000. doi: 10.1109/TITS.2020.2978438

Zhu B, Han J, Zhao J, et al. Combined hierarchical learning framework for personalized automatic lane-changing. IEEE Transactions on Intelligent Transportation Systems 2020; 22(10): 6275–6285. doi: 10.1109/TITS.2020.2990787

Han J, Zhao J, Zhu B, et al. Adaptive steering torque coupling framework considering conflict resolution for human-machine shared driving. IEEE Transactions on Intelligent Transportation Systems 2021; 23(8): 10983–10995. doi: 10.1109/TITS.2021.3098466

Zhang P, Zhu B, Zhao J, et al. Performance evaluation method for automated driving system in logical scenario. Automotive Innovation 2022; 5(3): 299–310.

Jiang Y, Zhu B, Yang S, et al. Vehicle trajectory prediction considering driver uncertainty and vehicle dynamics based on dynamic Bayesian network. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2022; 53(2): 689–703. doi: 10.1109/TSMC.2022.3186639

Zhao J, Du J, Zhu B, et al. Intelligent vehicle longitudinal cruise control based on adaptive dynamic sliding mode control. Automotive Engineering 2022; 44(1): 8–16. doi: 10.19562/j.chinasae.qcgc.2022.01.002

Zhu B, Wang Z, Zhao J, Tao X. Automatic parking space recognition method based on laser radar boundingboxes. In: Proceedings of the 2021 Annual Conference of the China Society of Automotive Engineers; 19–21 October 2021; Shanghai, China.