Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy Light Detection and Ranging (LiDAR) sensor and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association and tracking method to prevent mismatches utilizing point clouds' statistical features. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The proposed method is implemented in a small quadcopter, and the results show that our method can achieve the lowest position error (0.11m) and a comparable velocity error (0.23m/s) across the benchmarking algorithms running on the robot's onboard computer. The flight experiments prove that the tracking results from the proposed method can make the robot efficiently alter its trajectory for navigating dynamic environments. Our software is available on GitHub as an open-source ROS package.

翻译：在拥挤的室内环境中部署自主机器人通常需要其具备精确的动态障碍物感知能力。尽管自动驾驶领域已有大量工作研究了三维目标检测问题，但使用重型激光雷达（LiDAR）传感器获取的密集点云及其基于学习的数据处理产生的高计算成本，使得这些方法不适用于小型机器人（如配备小型机载计算机的视觉无人机）。为解决此问题，我们提出了一种基于RGB-D相机的轻量级三维动态障碍物检测与跟踪（DODT）方法，该方法专为计算能力有限的低功耗机器人设计。本方法采用新颖的集成检测策略，结合多个计算高效但低精度的检测器，以实现实时高精度障碍物检测。此外，我们引入了一种新的基于特征的数据关联与跟踪方法，利用点云的统计特征避免误匹配。系统还包含一个可选的辅助学习模块，用于增强障碍物检测范围和动态障碍物识别能力。所提方法已在小四旋翼无人机上实现，结果表明，在机器人机载计算机上运行的基准算法中，本方法实现了最低的位置误差（0.11米）和可比的 velocity 误差（0.23米/秒）。飞行实验证明，本方法的跟踪结果能使机器人有效改变轨迹以应对动态环境。我们的软件已作为开源ROS软件包发布在GitHub上。