An Integrated Visual System for Unmanned Aerial Vehicles Tracking and Landing on the Ground Vehicles

TL;DR

This paper presents an integrated UAV system for robust object detection, tracking, and landing on moving ground vehicles, demonstrating high accuracy and robustness in complex environments through simulation and real-world tests.

Contribution

The work introduces a novel LoG-based deep neural network for detection and tracking, and an iterative multi-model Kalman filter for motion estimation in UAV applications.

Findings

High robustness to scale and illumination changes

Effective in complex environments and real-world scenarios

Successful deployment in UAV tracking and obstacle avoidance

Abstract

The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original Kalman filter and designed an iterative multi-model-based filter to tackle the problem of unknown dynamics in real circumstances of motion estimations. Next, we implemented the whole system and do ROS Gazebo-based testing in two complicated circumstances to verify the effectiveness of our design. Finally, we have deployed the proposed detection, tracking, and motion estimation strategies into real applications to do UAV tracking of a pillar and obstacle avoidance. It is demonstrated that our system shows great accuracy and robustness in real applications.