A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera
This addresses the challenge of collision avoidance for quadcopters in crowded spaces, offering a real-time solution that is incremental by building on existing voxel-based methods with new filtering and prediction techniques.
The paper tackles the problem of real-time dynamic obstacle tracking and mapping for UAV navigation by proposing a system that uses an RGB-D camera to distinguish static and dynamic obstacles, achieving successful real-time tracking and safe obstacle avoidance in simulations and physical experiments.
The real-time dynamic environment perception has become vital for autonomous robots in crowded spaces. Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance. While plenty of sophisticated learning-based dynamic obstacle detection algorithms exist in autonomous driving, the quadcopter's limited computation resources cannot achieve real-time performance using those approaches. To address these issues, we propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. The proposed system first utilizes a depth image with an occupancy voxel map to generate potential dynamic obstacle regions as proposals. With the obstacle region proposals, the Kalman filter and our continuity filter are applied to track each dynamic obstacle. Finally, the environment-aware trajectory prediction method is proposed based on the Markov chain using the states of tracked dynamic obstacles. We implemented the proposed system with our custom quadcopter and navigation planner. The simulation and physical experiments show that our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles. Our software is available on GitHub as an open-source ROS package.