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This paper introduces PlaneLoc2 - a novel indoor global localization system designed to harness the potential of stereo cameras. A need for robust global localization that does not produce incorrect results (false positives) is present in almost every life-long autonomy task. We show that planar segments extracted from stereo vision data by a neural network enable such robust localization. Planar segments are easier to discriminate than keypoint features and provide easy-to-use geometric constraints. We propose an architecture that exploits a single deep neural network (DNN) to detect planar segments, produce appearance descriptors, and estimate segment geometry. Moreover, we introduce a novel view-based segment map and a novel pose retrieval procedure that considers the uncertainty of features to efficiently use the geometric constraints provided by them. We also show that the new learned descriptor provides better discrimination than the hand-crafted one. Finally, we present experimental results that show that our solution outperforms other state-of-the-art global localization methods and does not produce incorrect agent poses. For both test scenes it recognizes at least 15&#x0025; more poses than the second best method without incorrect recognitions.

PlaneLoc2: Indoor Global Localization Using Planar Segments and Passive Stereo Camera