Obstacle Tracking for Unmanned Surface Vessels using 3D Point Cloud

We present a method for detecting and tracking waterborne obstacles from an unmanned surface vehicle (USV) for the purpose of short-term obstacle avoidance. A stereo camera system provides a point cloud of the scene in front of the vehicle. The water surface is estimated by fitting a plane to the point cloud and outlying points are further processed to find potential obstacles. We propose a new plane fitting algorithm for water surface detection that applies a fast approximate semantic segmentation to filter the point cloud and utilizes an external IMU reading to constrain the plane orientation. A novel histogram-like depth appearance model is proposed to keep track of the identity of the detected obstacles through time and to filter out false detections, which negatively impact vehicle's automatic guidance system. The improved plane fitting algorithm and the temporal verification using depth fingerprints result in notable improvement on the challenging MODD2 dataset, by significantly reducing the amount of false positive detections. The proposed method is able to run in real time on board of a small-sized USV, which was used to acquire the MODD2 dataset as well.