An Online Calibration Method for Robust Multi-Modality 3D Object Detection

Multi-modality sensor fusion for 3D perception is a significant part for autonomous driving perception, which enables a comprehensive integration of different sensors and obtains a holistic understanding of the surrounding environment to improve accuracy, stability and reliability. Nevertheless, vibrations, collisions, and acceleration/deceleration in motion may result in a minor disturbance to the position of sensors, leading to offsets in sensor calibration. To address the limitations of the original method demanding considerable manual effort and time for hand-annotating checkerboards, we introduce an online Camera-LiDAR calibration method, which can perform calculations during the operation of autonomous vehicles to eliminate sensor biases. Different from the previous CNN-based online calibration algorithm, our approach differs in utilizing multi-scale features to accomplish alignment with a foundational ResNet and FPN Backbone alongside an attention-based multi-scale feature fusion module. Moreover, in the design of the loss function, we incorporate depth loss and point cloud loss in addition to the original smooth L1 norm loss to facilitate network backpropagation. Ultimately, our proposed method achieves an average translation error of 0.88 cm and a rotation error of 0.073° on the KITTI odometry dataset, which can significantly limit the discrepancies between sensors and thus enhance detection accuracy.