Abstract
Holistic visual navigation methods are an emerging alternative to the ubiquitous feature-based methods. Holistic methods match entire images pixel-wise instead of extracting and comparing local feature descriptors. In this paper we investigate which pixel-wise distance measures are most suitable for the holistic min-warping method with respect to illumination invariance. Two novel approaches are presented: tunable distance measures—weighted combinations of illumination-invariant and illumination-sensitive terms—and two novel forms of “sequential” correlation which are only invariant against intensity shifts but not against multiplicative changes. Navigation experiments on indoor image databases collected at the same locations but under different conditions of illumination demonstrate that tunable distance measures perform optimally by mixing their two portions instead of using the illumination-invariant term alone. Sequential correlation performs best among all tested methods, and as well but much faster in an approximated form. Mixing with an additional illumination-sensitive term is not necessary for sequential correlation. We show that min-warping with approximated sequential correlation can successfully be applied to visual navigation of cleaning robots.
Highlights
We first compare feature-based to holistic methods (Section 1.1), recapitulate the holistic min-warping method (Section 1.2), discuss the approaches to illumination invariance taken by feature-based and holistic methods (Section 1.3), and outline the contributions and the structure of the paper (Section 1.4).1.1
We presented a classification of holistic local visual homing methods in [20] which we recapitulate here in modified form for the reader’s convenience
Our experiments show that the performance of sequential correlation is better than that of normalized cross-correlation applied to edge-filtered columns without requiring a second, illumination-sensitive term
Summary
We first compare feature-based to holistic methods (Section 1.1), recapitulate the holistic min-warping method (Section 1.2), discuss the approaches to illumination invariance taken by feature-based and holistic methods (Section 1.3), and outline the contributions and the structure of the paper (Section 1.4).1.1. Feature-based methods (i) detect key-points like corners and only extract feature descriptors from their vicinity; (ii) perform complex transformations into feature descriptors and match feature descriptors between images; and (iii) typically require high-resolution images [1,2,3,4,5]. Matches between features are either used for estimating ego-motion between camera postures from two images (such as in visual odometry, see [13,14]), for estimating the metrical position of the corresponding landmark in a geometrical map [15,16,17,18], or for place recognition [19]. Together with subsequent outlier processing (like RANSAC) and n-point methods [14], feature-based methods can reliably estimate the relative posture between two images in 5 dimensions (up to scale)
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