EKF-Based Fusion System Improves UAV Distance Estimation for Search and Rescue Operations
Researchers have proposed a system that fuses depth camera measurements with monocular camera-based distance estimation using an Extended Kalman Filter (EKF) and deep learning to improve UAV-to-person distance tracking. The system uses YOLO-pose for real-time human body keypoint detection and has been validated against motion capture ground truth data in indoor tests. The approach reduces average distance estimation errors, RMSE, and standard deviations by up to 15.3%, with improved robustness in challenging conditions such as reflections and poor visibility relevant to search and rescue (SAR) scenarios.
A preprint submitted to IEEE describes a UAV subsystem designed to accurately estimate the distance between a drone and a human target for use in search and rescue (SAR) operations. The system fuses data from a depth camera and a monocular camera using an Extended Kalman Filter (EKF), with YOLO-pose providing real-time detection of human body keypoints to anchor distance calculations. By combining the complementary strengths of both camera modalities, the approach extends the effective depth detection range beyond the optimal working range of the depth camera alone. Indoor tests validated against motion capture ground truth data showed reductions in average error, RMSE, and standard deviation of up to 15.3% across three scenarios. The system demonstrated improved robustness under challenging conditions including surface reflections and low visibility, which are common in SAR environments. This work is presented as a subsystem within a broader framework for automatic people detection and face recognition using deep learning on drones. The paper has been submitted to IEEE for possible publication and is currently available as an arXiv preprint.
What's missing
The study was tested only in indoor environments; performance in outdoor or unstructured SAR field conditions remains unvalidated. The paper does not report results for occlusion scenarios or multi-person tracking, which are common real-world SAR challenges. Computational load and power consumption on actual drone hardware are not characterized, leaving practical deployment feasibility unclear.
What different sources said
- arXiv cs.AICenter
EKF-Based Depth Camera and Deep Learning Fusion for UAV-Person Distance Estimation and Following in SAR Operations
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