An MAE-aware ROI Sampling Model for LiDAR

Abstract

Light Detection and Ranging (LiDAR) sensors have relatively low resolutions, require considerable time to acquire the laser range measurement, and store large-scale point clouds. In order to address these issues, this paper presents a sampling algorithm which finds the optimal sampling rates in a region of interest (ROI) to minimize the total mean-absolute-error (MAE). Eventually, MAEs in both ROIs and overall scene decrease significantly. Experimental results show that the proposed scheme reduces the MAE in the object area by up to 63.3% and that in the overall scene by up to 34.2%.