Obstacle Tracking for Unmanned Surface Vessels Using 3-D Point Cloud
- Resource Type
- Periodical
- Authors
- Muhovic, J.; Mandeljc, R.; Bovcon, B.; Kristan, M.; Pers, J.
- Source
- IEEE Journal of Oceanic Engineering IEEE J. Oceanic Eng. Oceanic Engineering, IEEE Journal of. 45(3):786-798 Jul, 2020
- Subject
- Geoscience
Power, Energy and Industry Applications
Sea surface
Cameras
Three-dimensional displays
Collision avoidance
Visualization
Image segmentation
Path planning
3-D fingerprint
obstacle avoidance
obstacle tracking
unmanned surface vehicle (USV), visual stereo
- Language
- ISSN
- 0364-9059
1558-1691
2373-7786
In this paper, 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 that negatively impact the 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 data set 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 data set as well.