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 LiDAR 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 method to prevent mismatches and use the Kalman filter with the constant acceleration model to track detected obstacles. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The users can determine whether or not to run this module based on the available computation resources. The proposed method is implemented in a small quadcopter, and the experiments prove that the algorithm can make the robot detect dynamic obstacles and navigate dynamic environments safely.

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3D目标检测问题，但使用重型激光雷达获取的密集点云数据及其基于学习的处理方式所带来的高计算成本，使得这些方法难以适用于配备小型机载计算机的视觉无人机等小型机器人。针对这一问题，本文提出了一种基于RGB-D相机的轻量级三维动态障碍物检测与跟踪方法，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，通过结合多个计算高效但精度较低的检测器，实现了实时高精度障碍物检测。此外，我们引入了一种基于特征的数据关联方法以避免误匹配，并采用恒定加速度模型的卡尔曼滤波器对检测到的障碍物进行跟踪。系统还包含一个可选的辅助性学习模块，用于增强障碍物检测范围和动态障碍物识别能力，用户可根据可用计算资源决定是否启用该模块。该方法已在小型四旋翼无人机上实现，实验证明该算法能使机器人在动态环境中有效检测障碍物并实现安全导航。