This paper presents a point cloud downsampling algorithm for fast and accurate trajectory optimization based on global registration error minimization. The proposed algorithm selects a weighted subset of residuals of the input point cloud such that the subset yields exactly the same quadratic point cloud registration error function as that of the original point cloud at the evaluation point. This method accurately approximates the original registration error function with only a small subset of input points (29 residuals at a minimum). Experimental results using the KITTI dataset demonstrate that the proposed algorithm significantly reduces processing time (by 87\%) and memory consumption (by 99\%) for global registration error minimization while retaining accuracy.
@article{arxiv.2307.02948,
title = {Exact Point Cloud Downsampling for Fast and Accurate Global Trajectory Optimization},
author = {Kenji Koide and Shuji Oishi and Masashi Yokozuka and Atsuhiko Banno},
journal= {arXiv preprint arXiv:2307.02948},
year = {2023}
}
Comments
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2023)