Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor

• URL: http://arxiv.org/abs/2109.02786v1
• Date: Mon, 6 Sep 2021 23:51:27 GMT
• Title: Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor
• Authors: Tanaka Kanji
• Abstract summary: We consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner. Experiments using the public NCLT dataset validated the effectiveness of the proposed approach.
• Score: 5.482532589225552
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Landmark-based robot self-localization has recently garnered interest as a highly-compressive domain-invariant approach for performing visual place recognition (VPR) across domains (e.g., time of day, weather, and season). However, landmark-based self-localization can be an ill-posed problem for a passive observer (e.g., manual robot control), as many viewpoints may not provide an effective landmark view. In this study, we consider an active self-localization task by an active observer and present a novel reinforcement learning (RL)-based next-best-view (NBV) planner. Our contributions are as follows. (1) SIMBAD-based VPR: We formulate the problem of landmark-based compact scene description as SIMBAD (similarity-based pattern recognition) and further present its deep learning extension. (2) VPR-to-NBV knowledge transfer: We address the challenge of RL under uncertainty (i.e., active self-localization) by transferring the state recognition ability of VPR to the NBV. (3) NNQL-based NBV: We regard the available VPR as the experience database by adapting nearest-neighbor approximation of Q-learning (NNQL). The result shows an extremely compact data structure that compresses both the VPR and NBV into a single incremental inverted index. Experiments using the public NCLT dataset validated the effectiveness of the proposed approach.

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