Abstract
An unmanned surface vehicle (USV) is a speed boat that comes with a suite of sensors to understand the maritime environment and navigational intelligence to know how to navigate itself autonomously. Obstacle detection and mapping are mainly discussed in this paper by fusing data from a variety of sensors: a GPS, a compass, a monocular camera, and a radar. Firstly, a proposed motion stereo vision system is used for obstacle detection and mapping. Secondly, the targets respectively from radar and vision verify each other through a binary classifier trained by weighted ELM. Experiment results with test datasets from the real maritime environments are analyzed to illustrate the performance of this approach.
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