Detecting Ambiguity in Localization Problems Using Depth Sensors

We describe a method to identify ambiguous poses during tracking and localization based on depth sensors. In particular we distinguish between ambiguities related to specific acquisitions (track ambiguities) that hinder a good registration of the current pose with previous acquisitions and ambiguities related to repetitive elements observed in a particular (known) environment visited (map ambiguities). We propose a measure of both types of ambiguities to scale tracking and localization problems to large environments and to obtain more accurate results. We also propose a two level classifier that firstly labels an input observation as ambiguous or not, and then provides a prediction of candidate poses from the subset of unambiguous ones. We show that by identifying such poses, real time SLAM systems can reduce processing time in the real-time relocalization step. Furthermore, it permits the generation of more compact, highly-discriminative relocalization classifiers. We combine these proposals and use them as a proof of concept. Our preliminary results on real datasets justify the integration in SLAM or Ego-Motion pipelines of such concepts.