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