Causal calibration: iteratively calibrating LiDAR and camera by considering causality and geometry

Abstract

Abstract The external calibration between 3D LiDAR and 2D camera is an extremely important step towards multimodal fusion for robot perception. However, its accuracy is still unsatisfactory. To improve the accuracy of calibration, we first analyze the interference factors that affect the performance of the calibration model under a causal inference framework in this study. Guided by the causality analysis, we present Iter-CalibNet (Iterative Calibration Convolutional Neural Network) to infer a 6 degrees of freedom (DoF) rigid body transformation between 3D LiDAR and 2D camera. By downscaling point clouds to obtain more overlapping region between 3D–2D data pair and applying iterative calibration manner, the interference of confounding bias in the calibration model is effectively eliminated. Moreover, our Iter-CalibNet adds non-local neural network after each convolution operation to capture the transformation relationship. We also combine the geometric loss and photometric loss obtained from the interframe constraints to optimize the calibration accuracy. Extensive experiments demonstrate that our Iter-CalibNet can achieve leading performance by comparison with other CNN based and traditional calibration methods.

Keywords

• Causality analysis
• LiDAR and camera calibration
• Deep learning
• Geometry
• Machine vision