Mobile robots operating in natural terrain need some sort of long‐range perception in order
to navigate efficiently. Whereas LADAR is a commonly used sensor on such systems,
providing range data out to 25 m and beyond, we have instead focused on what information
can be extracted from vision. Our robot has only two stereo camera pairs for terrain sensing;
they provide reliable stereo data up to 5 m away, but this is not enough to prevent myopic
behavior. To overcome this problem, we have developed a novel approach to navigation …

Traditionally, path planning for field robotic systems is performed in Cartesian space: sensor
readings are transformed into terrain costs in a (Cartesian) costmap, and a path to the goal
is planned in that map. In this paper, we propose a new approach: planning a path for the
robot in the image-space of an on-board camera. We apply a learned color-to-cost mapping
to transform a raw image into a cost-image, which then undergoes a pseudo-configuration-
space transform. We search in the resulting cost-image for a path to the projected goal point …