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Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach

Nivand Khosravi, Meysam Basiri, Rodrigo Ventura

TL;DR

A lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework that enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure is presented.

Abstract

Accurate relative positioning is crucial for swarm aerial robotics, enabling coordinated flight and collision avoidance. Although vision-based tracking has been extensively studied, 3D LiDAR-based methods remain underutilized despite their robustness under varying lighting conditions. Existing systems often rely on bulky, power-intensive sensors, making them impractical for small UAVs with strict payload and energy constraints. This paper presents a lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework. Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non-repetitive scanning 3D LiDARs, ensuring reliable tracking while remaining suitable for small drones with strict payload constraints. Unlike conventional filtering techniques, the proposed method dynamically adjusts the noise covariance matrices using innovation and residual statistics, thereby enhancing tracking accuracy under real-world conditions. Additionally, a recovery mechanism ensures continuity of tracking during temporary detection failures caused by scattered LiDAR returns or occlusions. Experimental validation was performed using a Livox Mid-360 LiDAR mounted on a DJI F550 UAV in real-world flight scenarios. The proposed method demonstrated robust UAV tracking performance under sparse LiDAR returns and intermittent detections, consistently outperforming both standard Kalman filtering and particle filtering approaches during aggressive maneuvers. These results confirm that the framework enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure.

Lightweight 3D LiDAR-Based UAV Tracking: An Adaptive Extended Kalman Filtering Approach

TL;DR

A lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework that enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure is presented.

Abstract

Accurate relative positioning is crucial for swarm aerial robotics, enabling coordinated flight and collision avoidance. Although vision-based tracking has been extensively studied, 3D LiDAR-based methods remain underutilized despite their robustness under varying lighting conditions. Existing systems often rely on bulky, power-intensive sensors, making them impractical for small UAVs with strict payload and energy constraints. This paper presents a lightweight LiDAR-based UAV tracking system incorporating an Adaptive Extended Kalman Filter (AEKF) framework. Our approach effectively addresses the challenges posed by sparse, noisy, and nonuniform point cloud data generated by non-repetitive scanning 3D LiDARs, ensuring reliable tracking while remaining suitable for small drones with strict payload constraints. Unlike conventional filtering techniques, the proposed method dynamically adjusts the noise covariance matrices using innovation and residual statistics, thereby enhancing tracking accuracy under real-world conditions. Additionally, a recovery mechanism ensures continuity of tracking during temporary detection failures caused by scattered LiDAR returns or occlusions. Experimental validation was performed using a Livox Mid-360 LiDAR mounted on a DJI F550 UAV in real-world flight scenarios. The proposed method demonstrated robust UAV tracking performance under sparse LiDAR returns and intermittent detections, consistently outperforming both standard Kalman filtering and particle filtering approaches during aggressive maneuvers. These results confirm that the framework enables reliable relative positioning in GPS-denied environments without the need for multi-sensor arrays or external infrastructure.
Paper Structure (12 sections, 10 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 12 sections, 10 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. LiDAR-Based UAV Tracking
  4. Methodology
  5. LiDAR Data Processing and Object Detection
  6. Adaptive Kalman Filtering for UAV State Estimation
  7. Data Association and Occlusion Handling
  8. Experimental Result
  9. LiDAR Data Processing and Tracking
  10. Results
  11. Conclusion

Figures (4)

  • Figure 1: UAV Tracking System with AEKF and Recovery Logic. The system dynamically adapts process and measurement noise while incorporating track management for robustness against occlusions.
  • Figure 2: Flight test scenario showing both UAVs in operation. The observer UAV (left) is equipped with a Livox Mid-360 LiDAR, while the target UAV (right) performs randomized maneuvers for tracking evaluation across diverse ranges and orientations.
  • Figure 3: LiDAR processing pipeline: preprocessed point cloud (left), cluster extraction with statistics (center), and final UAV target identification (right).
  • Figure 4: Time-series comparison of UAV position trajectories along (X), (Y), and (Z) axes. RTK-GPS ground truth (black) compared with Adaptive CA-EKF (CAEKF, red), Fixed CA-KF (green), and Particle Filter (yellow). The CAEKF maintains high tracking fidelity, while the Fixed CA-KF exhibits significant divergence during measurement gaps ($t \approx 480$s, $500$s, $540$s), and the Particle Filter shows increased jitter during dynamic maneuvers.