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.

翻译：实时动态环境感知已成为拥挤空间中自主机器人不可或缺的能力。尽管基于体素的建图方法能够高效表示任意复杂形状的三维障碍物，但难以区分静态与动态障碍物，导致避障性能受限。虽然自动驾驶领域存在大量成熟的基于学习的动态障碍物检测算法，但四旋翼无人机有限的计算资源难以实现上述方法的实时性能。为解决这些问题，我们提出了一种基于RGB-D相机的四旋翼避障实时动态障碍物跟踪与建图系统。该系统首先利用深度图像与占据体素图生成潜在动态障碍物区域作为候选区域。基于障碍物区域候选，采用卡尔曼滤波器与连续性滤波器对每个动态障碍物进行跟踪。最后，基于马尔可夫链利用跟踪动态障碍物状态提出环境感知轨迹预测方法。我们在自主设计的四旋翼平台与导航规划器上实现了该系统。仿真与物理实验表明，我们的方法能够实时跟踪并表征动态环境中的障碍物，实现安全避障。相关软件以开源ROS功能包形式发布于GitHub。