Robust Object Recognition Using Wide Baseline Matching for RoboCup Applications

Abstract

As the RoboCup leagues evolve, higher requirements (e.g. object recognition skills) are imposed over the robot vision systems, which cannot be fulfilled using simple mechanisms as pure color segmentation or visual sonar. In this context the main objective of this article is to propose a robust object recognition system, based on the wide-baseline matching between a reference image (object model) and a test image where the object is searched. The wide baseline matching is implemented using local interest points and invariant descriptors. The proposed object recognition system is validated in two real-world tasks, recognition of objects in the RoboCup @Home league, and detection of robots in the humanoid league.KeywordsObject RecognitionReference ImageInterest PointHumanoid RobotScale Invariant Feature TransformThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.