Experimental Evaluation of 3D-LIDAR Camera Extrinsic Calibration
This work addresses calibration challenges for sensor fusion in robotics or autonomous systems, but it is incremental as it compares existing methods without introducing new ones.
The paper experimentally compared three target-based 3D-LIDAR camera calibration algorithms, evaluating their robustness to initialization and noise using metrics like Mean Line Re-projection Error, and provided recommendations on which algorithm to use under different conditions.
In this paper we perform an experimental comparison of three different target based 3D-LIDAR camera calibration algorithms. We briefly elucidate the mathematical background behind each method and provide insights into practical aspects like ease of data collection for all of them. We extensively evaluate these algorithms on a sensor suite which consists multiple cameras and LIDARs by assessing their robustness to random initialization and by using metrics like Mean Line Re-projection Error (MLRE) and Factory Stereo Calibration Error. We also show the effect of noisy sensor on the calibration result from all the algorithms and conclude with a note on which calibration algorithm should be used under what circumstances.