一种基于mae感知的激光雷达ROI采样模型

2020 International SoC Design Conference (ISOCC) Pub Date : 2020-10-21 DOI:10.1109/ISOCC50952.2020.9333034

Quan-Dung Pham, X. Nguyen, Hyuk-Jae Lee, Hyun Kim

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引用次数: 0

摘要

光探测和测距(LiDAR)传感器具有相对较低的分辨率，需要相当长的时间来获取激光距离测量，并且存储大规模的点云。为了解决这些问题，本文提出了一种采样算法，该算法在感兴趣区域(ROI)中找到最优采样率，以最小化总平均绝对误差(MAE)。最终，roi和整体场景的MAEs都显著降低。实验结果表明，该方案可将目标区域的MAE降低63.3%，将整个场景的MAE降低34.2%。

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An MAE-aware ROI Sampling Model for LiDAR

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%.

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2020 International SoC Design Conference (ISOCC)

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