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研究探讨了3D目标检测问题，但使用重载激光雷达（LiDAR）传感器生成的高密度点云以及基于学习的数据处理带来的高计算成本，使得这些方法不适用于小型机器人（例如搭载小型机载计算机的视觉无人机）。针对这一问题，本文提出了一种基于RGB-D相机的轻量级3D动态障碍物检测与跟踪方法（DODT），专为计算能力有限的低功耗机器人设计。该方法采用新颖的集成检测策略，结合多个计算高效但精度较低的检测器，实现实时高精度障碍物检测。此外，我们还引入了一种基于特征的数据关联与跟踪方法，利用点云的统计特征防止误匹配。系统中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。所提方法已在小型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，该方法实现了最低的位置误差（0.11米）和可比的速率误差（0.23米/秒）。飞行实验证明，该方法输出的跟踪结果可使机器人有效调整轨迹以适应动态环境。我们的软件已作为开源ROS包发布在GitHub上。