Improvements to Target-Based 3D LiDAR to Camera Calibration

TL;DR: The use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in aLiDAR image of a target, and a fitting method for the LiDAR to monocular camera transformation that avoids the tedious task of target edge extraction from the point cloud.

Abstract: The rigid-body transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm reprojection errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors in their distance measurements. This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, (2) a fitting method for the LiDAR to monocular camera transformation that avoids the tedious task of target edge extraction from the point cloud, and (3) a “cross-validation study” based on projection of the 3D LiDAR target vertices to the corresponding corners in the camera image. The end result is a 50% reduction in projection error and a 70% reduction in its variance with respect to baseline.