一种基于RGB-D相机的无人机导航与避障实时动态障碍物跟踪与建图系统
A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera
AI总结：
本文提出一种基于RGB-D相机的四旋翼实时动态障碍物跟踪与建图系统，通过体素地图生成候选区域、卡尔曼滤波与连续性滤波跟踪障碍物，并利用马尔可夫链进行环境感知轨迹预测，实验验证了其在动态环境中的实时避障能力。
摘要：
实时动态环境感知对于拥挤空间中的自主机器人已变得至关重要。尽管流行的基于体素的建图方法能够高效表示具有任意复杂形状的三维障碍物，但它们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复杂的基于学习的动态障碍物检测算法，但四旋翼飞行器有限的计算资源无法使用这些方法实现实时性能。为解决这些问题，我们提出了一种使用RGB-D相机的四旋翼避障实时动态障碍物跟踪与建图系统。所提出的系统首先利用深度图像和占据体素地图生成潜在的动态障碍物区域作为候选区域。基于障碍物区域候选，应用卡尔曼滤波器和我们设计的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，利用被跟踪动态障碍物的状态，提出了环境感知的轨迹预测方法。我们在自制的四旋翼飞行器和导航规划器上实现了所提出的系统。仿真和物理实验表明，我们的方法能够在动态环境中实时成功地跟踪和表示障碍物，并安全地避开障碍物。我们的软件已在GitHub上作为开源ROS软件包提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
