UAV Position Estimation using a LiDAR-based 3D Object Detection Method
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912318330241024 |
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| author | Olawoye, Uthman Gross, Jason N. |
| author_facet | Olawoye, Uthman Gross, Jason N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UAV Position Estimation using a LiDAR-based 3D Object Detection Method Olawoye, Uthman Gross, Jason N. Robotics 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. |
| title | UAV Position Estimation using a LiDAR-based 3D Object Detection Method |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.07028 |