用激光雷达+深度学习实现无人机在无GPS环境下的相对定位
UAV Position Estimation using a LiDAR-based 3D Object Detection Method
- 基于点云分柱结构与2D CNN的PointPillars算法检测无人机
- 相比传统方法，定位误差更小，精度提升明显
- 适合需要高精度定位的无人系统协同场景
本文研究在无GPS环境下，利用安装在无人地面车辆（UGV）上的激光雷达传感器，通过深度学习的3D目标检测方法计算无人飞行器（UAV）的相对位置。通过评估激光雷达数据，采用PointPillars算法进行检测，该算法结合列式体素点云表示与2D卷积神经网络（CNN），提取能区分目标的点云特征，识别对象为无人机。当前定位方法依赖点云分割、欧氏聚类及预设启发式规则获取无人机相对位置。将两种方法的结果与参考真值解进行对比，验证了新方法的有效性。
原文摘要 · Abstract (English)
This paper explores the use of applying a deep learning approach for 3D object detection to compute the relative position of an Unmanned Aerial Vehicle (UAV) from an Unmanned Ground Vehicle (UGV) equipped with a LiDAR sensor in a GPS-denied environment. This was achieved by evaluating the LiDAR sensor's data through a 3D detection algorithm (PointPillars). The PointPillars algorithm incorporates a column voxel point-cloud representation and a 2D Convolutional Neural Network (CNN) to generate distinctive point-cloud features representing the object to be identified, in this case, the UAV. The current localization method utilizes point-cloud segmentation, Euclidean clustering, and predefined heuristics to obtain the relative position of the UAV. Results from the two methods were then compared to a reference truth solution.
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