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A new obstacle detection algorithm for unmanned surface vehicles (USVs) ispresented. A state-of-the-art graphical model for semantic segmentation isextended to incorporate boat pitch and roll measurements from the on-boardinertial measurement unit (IMU), and a stereo verification algorithm thatconsolidates tentative detections obtained from the segmentation is proposed.The IMU readings are used to estimate the location of horizon line in theimage, which automatically adjusts the priors in the probabilistic semanticsegmentation model. We derive the equations for projecting the horizon intoimages, propose an efficient optimization algorithm for the extended graphicalmodel, and offer a practical IMU-camera-USV calibration procedure. Using an USVequipped with multiple synchronized sensors, we captured a new challengingmulti-modal dataset, and annotated its images with water edge and obstacles.Experimental results show that the proposed algorithm significantly outperformsthe state of the art, with nearly 30% improvement in water-edge detectionaccuracy, an over 21% reduction of false positive rate, an almost 60% reductionof false negative rate, and an over 65% increase of true positive rate, whileits Matlab implementation runs in real-time.

Stereo obstacle detection for unmanned surface vehicles by IMU-assisted semantic segmentation