GASP: Geometric Association with Surface Patches

Abstract

A fundamental challenge to sensory processing tasks in perception and robotics is the problem of obtaining data associations across views. We present a robust solution for ascertaining potentially dense surface patch (super pixel) associations, requiring just range information. Our approach involves decomposition of a view into regularized surface patches. We represent them as sequences expressing geometry invariantly over their super pixel neighborhoods. Uniquely consistent partial orderings are utilized for the same. We match these representations robustly through an optimal sequence comparison metric based on the Damerau-Levenshtein distance. The approach is able to perform under wide baselines, heavy rotations, significant occlusions and partially overlapping content. The technique does not require any priors -- motion or otherwise, and does not make restrictive assumptions on scene structure and sensor movement. It does not require appearance -- is hence more widely applicable than appearance reliant methods, and invulnerable to related ambiguities such as texture less or aliased content. We present promising qualitative and quantitative results under diverse settings, along with comparatives with popular approaches based on range as well as RGB-D data.