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

Source: [UAV Position Estimation using a LiDAR-based 3D Object Detection Method](<https://www.sota2.com/research/paper/68fa77f41e209131cea762f8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:29:27.078Z

Authors: Uthman Olawoye, Jason N. Gross

Published: 2025-04-09T16:43:59.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2504.07028>)

[PDF](<https://arxiv.org/pdf/2504.07028.pdf>)