Abstract
SummaryVisual 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.
Highlights
Modern applications for robots demand the ability to deal with unstructured and unknown terrain, such as exploration on planetary surfaces, disaster sites, and underground tunnels.[1]
For field of view (FOV) higher than this, we do not see much performance improvement, and for the widest FOV tested (354◦), there is a small but consistent drop in performance. We refer to this phenomenon as the ‘blinker’ effect and we show how it is a consequence of the trade-off in the advantage of wider FOVs for handling larger homing offset angles against the relative quality of feature matching in larger images
This paper has evaluated the effect of varying the size of the horizontal FOV in stereo-vision-based visual homing
Summary
Modern applications for robots demand the ability to deal with unstructured and unknown terrain, such as exploration on planetary surfaces, disaster sites, and underground tunnels.[1] Navigation is challenging for an autonomous robot operating in an unstructured environment, and several approaches to this problem have been investigated. These include the generation of metric maps using algorithms, such as SLAM2 followed by explicit path-planning,[3] as well as the construction of a qualitative, topological map[4] and the associated techniques for navigating locally to and from map nodes.
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