| 标 题： |
3D Keypoint Detectors from Scanning Mirror LIDAR Scans for Localization of Lunar Rovers
基于扫描镜激光雷达数据的3D关键点检测器，用于月球车定位 |
| 作 者： |
Tuan Dinh Luong
Jordan Larson |
| 关键词： |
Lunar rover [月球车]
LIDAR [ 激光雷达]
Keypoint detectors [ 关键点检测器] |
| 出版年： |
2025 |
| 文集名： |
2025 IEEE/ION Position, Location and Navigation Symposium (PLANS 2025)
2025年IEEE/ION位置、定位与导航研讨会（PLANS 2025） |
| 会议名： |
2025 IEEE/ION Position, Location and Navigation Symposium (PLANS 2025)
2025年IEEE/ION位置、定位与导航研讨会（PLANS 2025） |
| 届 次： |
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| 摘 要： |
This paper presents the implementation and evaluation of multiple 3D keypoint detectors on LiDAR scans for rover terrain relative navigation (TRN) on the Lunar surface. One method utilizes morphological dilation to detect positive objects while the other utilizes normals analysis and raytracing to detect craters. These detectors serve as front ends to many localization solutions by reducing a point cloud down to a set of salient keypoints that can be tracked between scans. A novel implementation of the Random Finite Set (RFS) based Rao-Blackwellized Particle Filter Simultaneous Localization and Mapping (SLAM) algorithm is used to process these keypoints due to the RFS framework's ability to robustly handle typical measurement characteristics statistics such as noise, missed detection, and false alarm with an elegant approach to the data association problem. More specifically, this paper implements the Probabilistic Hypothesis Density (PHD)-SLAM to Lunar rover TRN for two different 3D keypoint detectors. The proposed methods are evaluated using synthetic data obtained from a simulated micro-electromechanical system (MEMS) LiDAR in a procedurally generated Lunar environment based on the NVIDIA Omniverse framework. Implementation details of this work is provided in github.com/TL-4319/RFS_SLAM_OmniLRS.
本文介绍了在月球表面火星车地形相对导航（TRN）中，对LiDAR扫描数据进行多种3D关键点检测器的实现与评估。其中一种方法利用形态学膨胀来检测正向物体，另一种则通过法线分析和光线追踪来检测陨石坑。这些检测器作为多种定位解决方案的前端，可将点云简化为一组突出的关键点，并可在不同扫描之间进行跟踪。本文采用了一种基于随机有限集（RFS）的Rao-Blackwell化粒子滤波器同时定位与建图（SLAM）算法的新实现方式，用于处理这些关键点。这是因为RFS框架能够以优雅的方式解决数据关联问题，稳健地应对噪声、漏检和误报等典型测量特性统计问题。具体而言，本文针对两种不同的3D关键点检测器，实现了概率假设密度（PHD）-SLAM算法用于月球火星车的TRN导航。所提出的方法使用基于NVIDIA Omniverse框架的程序生成月球环境中的模拟微机电系统（MEMS）LiDAR获取的合成数据进行评估。本工作的实现细节请参见 github.com/TL-4319/RFS_SLAM_OmniLRS。 |
| 中文内容为网络AI翻译，专业术语未必准确，仅供参考。 |
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