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arXiv · 2504.07028

UAV Position Estimation using a LiDAR-based 3D Object Detection Method

Uthman Olawoye·Jason N. Gross
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Abstract

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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Uthman Olawoye, Jason N. Gross. 2025-04-09. UAV Position Estimation using a LiDAR-based 3D Object Detection Method. https://doi.org/10.1109/plans53410.2023.10139979

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