# Detection and Tracking of MAVs Using a Rosette Scanning Pattern LiDAR

arXiv: https://arxiv.org/abs/2408.08555 · HTML: https://mlsanity.com/paper/2408.08555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 16 of 16)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Deep Learning for Visual Tracking: A Comprehensive Survey](https://mlsanity.com/paper/1912.00535/) (2019)
- [A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?](https://mlsanity.com/paper/1912.03251/) (2019)
- [Weather Influence and Classification with Automotive Lidar Sensors](https://mlsanity.com/paper/1906.07675/) (2019)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Drone Detection Using Depth Maps](https://mlsanity.com/paper/1808.00259/) (2018)
- [Drone Detection and Tracking in Real-Time by Fusion of Different Sensing Modalities](https://mlsanity.com/paper/2207.01927/) (2022)
- [Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking](https://mlsanity.com/paper/2402.09865/) (2024)
- [Wavelet Transform Analytics for RF-Based UAV Detection and Identification System Using Machine Learning](https://mlsanity.com/paper/2102.11894/) (2021)
- [Detection and Tracking of Small Objects in Sparse 3D Laser Range Data](https://mlsanity.com/paper/1903.05889/) (2019)
- [Adaptive Lidar Scan Frame Integration: Tracking Known MAVs in 3D Point Clouds](https://mlsanity.com/paper/2103.04069/) (2021)
- [UAV Tracking with Solid-State Lidars:Dynamic Multi-Frequency Scan Integration](https://mlsanity.com/paper/2304.12125/) (2023)

## Abstract (arXiv)

The use of commercial Micro Aerial Vehicles (MAVs) has surged in the past decade, offering societal benefits but also raising risks such as airspace violations and privacy concerns. Due to the increased security risks, the development of autonomous drone detection and tracking systems has become a priority. In this study, we tackle this challenge, by using non-repetitive rosette scanning pattern LiDARs, particularly focusing on increasing the detection distance by leveraging the characteristics of the sensor. The presented method utilizes a particle filter with a velocity component for the detection and tracking of the drone, which offers added re-detection capability. A Pan-Tilt platform is utilized to take advantage of the specific characteristics of the rosette scanning pattern LiDAR by keeping the tracked object in the center where the measurement is most dense. The detection capabilities and accuracy of the system are validated through indoor experiments, while the maximum detection distance is shown in our outdoor experiments. Our approach achieved accuracy on par with the state-of-the-art indoor method while increasing the maximum detection range by approximately 80% beyond the state-of-the-art outdoor method.