Saliency-Based Obstacle Detection and Ground-Plane Estimation for Off-Road Vehicles

Due to stringing time constraints, saliency models are becoming popular tools for building situated robotic systems requiring, for instance, object recognition and vision-based localisation capabilities. This paper contributes to this endeavour by applying saliency into two new tasks: modulation of stereobased obstacle detection and ground-plane estimation, both to operate on-board off-road vehicles. To achieve this, a new biologically inspired saliency model, along with a set of adaptations to the task-specific algorithms, are proposed. Experimental results show a reduction in computational cost and an increase in both robustness and accuracy when saliency modulation is used.