Mining Minimal Map-Segments for Visual Place Classifiers

Kanji, Tanaka

doi:10.31224/osf.io/v74eq

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…This formulation requires to partition the robot workspace into place classes. The concept of place partitioning is an important topic of on-going research in the field of robot self-localization (e.g., [24], [25]), as was demonstrated in our previous study [26]. In this study, we loosely follow the relatively simple equal-spaced partitioning method [27], and partition the workspace into 40m×40m square regions.…”

Section: B Localizermentioning

confidence: 99%

Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

Kanji¹

2020

Preprint

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Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most of the existing methods rely on place-specific background modeling. The time/space cost for such place-specific models becomes prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically tailored for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where N multiple localizers are capable of mutual diagnosis, thus not requiring any explicit background modeling/model. We extend this multi-map diagnosis approach toward a more generic single-map-based object-level diagnosis framework (i.e., ICD), where state-of-the-art self-localization systems can be used in their original form, which is as the change object indicator. The available single localizer is extended to different N localizers by introducing different N masked input images. Further, we also consider map diagnosis on a state-of-the-art deep-visual-SLAM system (rather than on conventional bag-of-words or landmark -based systems) in which the blackbox nature of the deep convolutional neural network (DCN) complicates the diagnosis problem. We also consider a 3D point cloud (PC) -based SLAM, and for the first time (to the best of our knowledge) adopt the state-of-the-art scan context PC descriptor for the purpose of map diagnosis.

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Section: B Localizermentioning

confidence: 99%

Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

Kanji¹

2020

Preprint

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“…Such landmark-based self-localization problem has two unique challenges. (1) Landmark selection: In offline training stage, the robot must learn the main landmarks that represent the robot workspace, in either self-supervised or unsupervised manner [5]. (2) Next-Best-View (NBV): In online selflocalization stage, the robot must determine NBVs to reidentify such spatially sparse landmarks as many as possible [6].…”

Section: Introductionmentioning

confidence: 99%

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

Kanji¹

2021

Preprint

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Landmark-based robot self-localization has attracted recent research interest as an efficient maintenance-free approach to visual place recognition (VPR) across domains (e.g., times of the day, weathers, seasons). 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 effective landmark view. Here, we consider active self-localization task by an active observer, and present a novel reinforcement-learning (RL) -based next-best-view (NBV) planner. Our contributions are summarized as follows. (1) SIMBAD-based VPR: We present a landmark ranking -based compact scene descriptor by introducing a deep-learning extension of similarity-based pattern recognition (SIMBAD). (2) VPR-to-NBV knowledge transfer: We tackle the challenge of RL under uncertainty (i.e., active self-localization) by transferring the VPR's state recognition ability to NBV. (3) NNQL-based NBV: We view the available VPR as the experience database by adapting a nearest-neighbor -based approximation of Q-learning (NNQL). The result is an extremely compact data structure that compresses both the VPR and NBV modules into a single incremental inverted index. Experiments using public NCLT dataset validate the effectiveness of the proposed approach.

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Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection

Kanji

2021

JACIII

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Although image change detection (ICD) methods provide good detection accuracy for many scenarios, most existing methods rely on place-specific background modeling. The time/space cost for such place-specific models is prohibitive for large-scale scenarios, such as long-term robotic visual simultaneous localization and mapping (SLAM). Therefore, we propose a novel ICD framework that is specifically customized for long-term SLAM. This study is inspired by the multi-map-based SLAM framework, where multiple maps can perform mutual diagnosis and hence do not require any explicit background modeling/model. We extend this multi-map-based diagnosis approach to a more generic single-map-based object-level diagnosis framework (i.e., ICD), where the self-localization module of SLAM, which is the change object indicator, can be used in its original form. Furthermore, we consider map diagnosis on a state-of-the-art deep convolutional neural network (DCN)-based SLAM system (instead of on conventional bag-of-words or landmark-based systems), in which the blackbox nature of the DCN complicates the diagnosis problem. Additionally, we consider a three-dimensional point cloud (PC)-based (instead of typical monocular color image-based) SLAM and adopt a state-of-the-art scan context PC descriptor for map diagnosis for the first time.

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Mining Minimal Map-Segments for Visual Place Classifiers

Cited by 3 publications

References 37 publications

Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

Fault-Diagnosing DCN-SLAM for 3D Change Object Detection: A Method based on Masking Input Images

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

Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection

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