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.Comment: 8 pages, 12 figures, 2 table