Degree of Contribution

Lead

Document Type

Article

Keywords

Robotics, Computer Vision, Stereovision, Visual Homing, Navigation

Disciplines

Computer Engineering | Robotics

Abstract

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.

Publication Title

Robotica

Volume

ONLINE FIRST doi:10.1017/S0263574719001061

Article Number

1072

Publication Date

Summer 7-3-2019

First Page

1

Last Page

17

Extent

17

DOI of Published Version

doi:10.1017/S0263574719001061

Publisher

Cambridge University Press

Language

English

Peer Reviewed

1

Version

Published

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Included in

Robotics Commons

Share

COinS