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.

提出了一种基于 RGB-D 相机的实时动态障碍物跟踪和建图系统，该系统使用 “占用格子图” 生成动态障碍物区域，应用卡尔曼滤波和连续滤波来跟踪每个动态障碍物，并提出了一种基于 Markov 链的环境感知轨迹预测方法。实验结果表明，该方法可以在动态环境中实时成功跟踪和避免障碍物。