Techniques for Evaluating Optical Flow for Visual Odometry in Extreme Terrain

Abstract

Motion vision (visual odometry, the estimation of camera egomotion) is a well researched field, yet has seen relatively limited use despite strong evidence from biological systems that vision can be extremely valuable for navigation. The limited use of such vision techniques has been attributed to a lack of good algorithms and insufficient computer power, but both of those problems were resolved as long as a decade ago. A gap presently yawns between theory and practice, perhaps due to perceptions of robot vision as less reliable and more complex than other types of sensing. We present an experimental methodology for assessing the real world precision and reliability of visual odometry techniques in both normal and extreme terrain. This paper evaluates the performance of a mobile robot equipped with a simple vision system in common outdoor and indoor environments, including grass, pavement, ice, and carpet. Our results show that motion vision algorithms can be robust and effective, and suggest a number of directions for further development.