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JACIII Vol.25 No.3 pp. 356-364
doi: 10.20965/jaciii.2021.p0356
(2021)

Paper:

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

Kanji Tanaka

Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

Received:
April 2, 2020
Accepted:
March 23, 2021
Published:
May 20, 2021
Keywords:
SLAM, diagnosing, deep neural network, 3D change detection
Abstract
Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection

Masking input images for subimage-level change detection using a deep visual SLAM system

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.

Cite this article as:
Kanji Tanaka, “Fault-Diagnosing Deep-Visual-SLAM for 3D Change Object Detection,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.3, pp. 356-364, 2021.
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