cs.ROMar 10, 2026

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

Authors: Nivand Khosravi, Meysam Basiri, Rodrigo Ventura

Organizations: Institute for Systems and Robotics (ISR), Instituto Superior Técnico, Lisbon, Portugal

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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.

Figures & tables

Figure 1: Complete system architecture: LiDAR point cloud processing and cluster extraction (Stage I), data association and recovery logic (Stage II), and Adaptive EKF with online QkQ_{k} / RkR_{k} tuning (Stage III). The feedback arrow from the EKF predicted state x^k−\hat{x}_{k}^{-} to the association gate closes the estimation loop.
ApproachConfigurationSensorCore MethodKey Limitation
Dogru & Marques (2022) [ 11 ]Ground  ⁣→ ⁣\!\to\! DroneVelodyne VLP-16Track-before-detect + Bayesian filterHigh compute; fixed noise model
Catalano et al. (2023) [ 10 ]Ground  ⁣→ ⁣\!\to\! DroneSolid-state LiDARMulti-scan EKF (adaptive scan count only)Ground-only; no noise adaptation
Gazdag et al. (2024) [ 13 ]Pan-tilt  ⁣→ ⁣\!\to\! DroneLivox AVIA (rosette)Particle filter + background subtractionBulky pan-tilt; fixed noise model
Sier et al. (2023) [ 14 ]Fixed ground  ⁣→ ⁣\!\to\! DroneOuster (LiDAR-image)YOLOv5 tracking-by-detectionFixed platform; ML inference cost
Leuci et al. (2024) [ 3 ]UAV+UGV  ⁣→ ⁣\!\to\! DroneCamera + LiDARMulti-sensor fusion + KFCalibration burden; fixed noise
Chen et al. (2016) [ 16 ]UAV (self-navigation)2D laser + IMUQP trajectory optimisationNavigation only; no target tracking
Table 1: Representative LiDAR-Based UAV Tracking Approaches.
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: Sequential stages of the LiDAR preprocessing and target-detection pipeline. From left to right: raw downsampled point cloud, Euclidean clustering with cluster statistics, and final drone candidate selection after geometric validation and Mahalanobis gating.
Figure 4: Time-series comparison of UAV position along X, Y, and Z axes. RTK ground truth (black), Adaptive CA-EKF / CAEKF (red), Fixed CA-KF (green), and Particle Filter (yellow). The CAEKF maintains high tracking fidelity, while the Fixed CA-KF diverges during measurement gaps ( t≈480t\approx 480 s, 500500 s, 540540 s) and the Particle Filter shows increased jitter during dynamic maneuvers.
Metric (Unit)Fixed CA-KFParticle FilterCAEKF (Ours)
(A) Tracking Accuracy RTK ground truth
RMSE X (m)15.06.03.0
RMSE Y (m)20.08.04.0
RMSE Z (m)4.02.51.5
RMSE 3D (m)13.05.52.8
(B) Computational Performance Jetson Nano (4-core ARM)
Table 2: Tracking accuracy and computational metrics on Jetson Nano under aggressive-maneuver flight. †\dagger Peak values elevated only during transient adaptation bursts, within four-core capacity. ‡\ddagger Lower valid-measurement rate reflects stricter adaptive gating a design strength, not a limitation.
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