Visual homing is a local navigation technique used to direct a robot to a previously seen location by comparing the image of the original location with the current visual image. Prior work has shown that exploiting depth cues such as image scale or stereo-depth in homing leads to improved homing performance. While it is not unusual to use a panoramic field of view (FOV) camera in visual homing, it is unusual to have a panoramic FOV stereo-camera. So, while the availability of stereo-depth information may improve performance, the concomitant-restricted FOV may be a detriment to performance, unless specialized stereo hardware is used. In this paper, we present an investigation of the effect on homing performance of varying the FOV widths in a stereo-vision-based visual homing algorithm using a common stereo-camera. We have collected six stereo-vision homing databases – three indoor and three outdoor. Based on over 350,000 homing trials, we show that while a larger FOV yields performance improvements for larger homing offset angles, the relative improvement falls off with increasing FOVs, and in fact decreases for the widest FOV tested. We conduct additional experiments to identify the cause of this fall-off in performance, which we term the ‘blinder’ effect, and which we predict should affect other correspondence-based visual homing algorithms.

Visual Homing is a navigation method based on comparing a stored image of a goal location to the current image to determine how to navigate to the goal location. It is theorized that insects such as ants and bees employ visual homing techniques to return to their nest or hive, and inspired by this, several researchers have developed elegant robot visual homing algorithms. Depth information, from visual scale, or other modality such as laser ranging, can improve the quality of homing. While insects are not well equipped for stereovision, stereovision is an effective robot sensor. We describe the challenges involved in using stereovision derived depth in visual homing and our proposed solutions. Our algorithm, Homing with Stereovision (HSV), utilizes a stereo camera mounted on a pan-tilt unit to build composite wide-field stereo images and estimate distance and orientation from the robot to the goal location. HSV is evaluated in a set of 200 indoor trials using two Pioneer 3-AT robots showing it effectively leverages stereo depth information when compared to a depth from scale approach.