Improvements to Target-Based 3D LiDAR to Camera Calibration
This work addresses a crucial but incremental improvement in sensor calibration for autonomous systems, enhancing accuracy in tasks like SLAM.
The paper tackles the problem of accurately calibrating LiDAR to monocular camera transformations for sensor fusion in autonomous systems, proposing the use of known-dimension targets and a fitting method that prioritizes camera data, resulting in reduced errors such as mitigating 20 cm translation errors at 5 m distances.
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.