LAUSR

As the acquisition of laser range measurements such as those from light detection and ranging (LiDAR) sensors requires a considerable amount of time, to design an effective sampling algorithm is a critical task in numerous laser range applications. The state-of-the-art adaptive methods such as two-step sampling are highly effective at handling less complex scenes such as indoor environments with a moderately low sampling rate. However, their performance is relatively low in complex on-road environments, particularly when the sampling rate of the measuring equipment is low. To address this problem, this paper proposes a region-of-interest (ROI)-based sampling algorithm in on-road environments for autonomous driving. With the aid of fast and accurate road and object detection algorithms, particularly those based on convolutional neural networks, the proposed sampling algorithm utilizes the semantic information and effectively distributes samples in the road, object, and background areas. The experimental results demonstrate that the proposed algorithm significantly reduces the mean-absolute-error in the object area by at most 52.8% compared to two-step sampling; moreover, it achieves robust reconstruction quality even at a very low sampling rate of 1%.