Improvements to Target-Based 3D LiDAR to Camera Calibration

TL;DR

This paper presents improved methods for calibrating the spatial relationship between LiDAR sensors and monocular cameras, using known targets and a fitting approach that prioritizes camera data accuracy to enhance calibration precision.

Contribution

It introduces a target-based approach combined with a fitting method that leverages the accuracy of camera images to improve LiDAR-camera calibration.

Findings

Enhanced calibration accuracy with target-based methods

Reduction in systematic errors in LiDAR measurements

Improved alignment between LiDAR and camera data

Abstract

The homogeneous 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 translation 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, and (2) a fitting method for the LiDAR to monocular camera transformation that fundamentally assumes the camera image data is the most accurate information in one's possession.