A high frequency 3D LiDAR with enhanced measurement density via Papoulis-Gerchberg

Bengisu Ozbay, Elvan Kuzucu, M. Gul, Dilan Ozturk, M. Tasci, A. Arisoy, H. Sirin, Ismail Uyanik
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引用次数: 4

Abstract

Light Detection and Ranging (LiDAR) devices are gaining more importance for obtaining sensory information in mobile robot applications. However, existing solutions in literature yield low frequency outputs with huge measurement delay to obtain 3D range image of the environment. This paper introduces the design and construction of a 3D range sensor based on rotating a 2D LiDAR around its pitch axis. Different than previous approaches, we adjust our scan frequency to 5 Hz to support its application on mobile robot platforms. However, increasing scan frequency drastically reduces the measurement density in 3D range images. Therefore, we propose two post-processing algorithms to increase measurement density while keeping the 3D scan frequency at an acceptable level. To this end, we use an extended version of the Papoulis-Gerchberg algorithm to achieve super-resolution on 3D range data by estimating the unmeasured samples in the environment. In addition, we propose a probabilistic obstacle reconstruction algorithm to consider the probabilities of the estimated (virtual) points and to obtain a very fast prediction about the existence and shape of the obstacles.
通过Papoulis-Gerchberg增强测量密度的高频3D激光雷达
在移动机器人的应用中,光探测和测距(LiDAR)设备对于获取感官信息越来越重要。然而,文献中现有的解决方案在获取环境的三维距离图像时,输出频率低,测量延迟大。本文介绍了一种基于二维激光雷达绕俯仰轴旋转的三维距离传感器的设计与构造。与之前的方法不同,我们将扫描频率调整为5 Hz,以支持其在移动机器人平台上的应用。然而,增加扫描频率大大降低了三维范围图像的测量密度。因此,我们提出了两种后处理算法来增加测量密度,同时将3D扫描频率保持在可接受的水平。为此,我们使用扩展版的Papoulis-Gerchberg算法,通过估计环境中未测量的样本来实现3D距离数据的超分辨率。此外,我们提出了一种概率障碍重建算法来考虑估计(虚拟)点的概率,并获得关于障碍物存在和形状的非常快速的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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