利用 RGB-D 相机实现自主机器人导航的机载动态目标检测与跟踪
Onboard dynamic-object detection and tracking for autonomous robot navigation with RGB-D camera
AI总结：
针对小型低功耗机器人在拥挤室内的动态避障需求，提出基于 RGB-D 相机的轻量级 DODT 方法，集成多检测器并结合点云统计特征跟踪，机载实验取得最低位置误差。
摘要：
在拥挤的室内环境中部署自主机器人，通常要求其具备准确的动态障碍物感知能力。尽管自动驾驶领域已有大量先前工作研究了三维目标检测问题，但这些方法使用来自笨重激光雷达（LiDAR）传感器的密集点云，并且基于学习的数据处理计算成本高昂，使其不适用于小型机器人，例如搭载小型机载计算机的基于视觉的无人机。为解决这一问题，我们提出一种基于 RGB-D 相机的轻量级三维动态障碍物检测与跟踪（DODT）方法，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，将多个计算高效但精度较低的检测器结合起来，以实现实时、高精度的障碍物检测。此外，我们引入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防止误匹配。同时，我们的系统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。所提方法在一架小型四旋翼飞行器上实现，结果表明，在机器人机载计算机上运行的所有基准算法中，该方法取得了最低的位置误差（0.11 米），以及具有可比性的速度误差（0.23 米/秒）。飞行实验证明，所提方法的跟踪结果能够使机器人高效改变轨迹，从而在动态环境中导航。我们的软件已作为开源 ROS 软件包在 GitHub 上发布。
英文摘要：
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.
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