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In this paper, we present a new fault diagnosis (FD) -based approach for detection of imagery changes that can detect significant changes as inconsistencies between different sub-modules (e.g., self-localizaiton) of visual SLAM. Unlike classical change detection approaches such as pairwise image comparison (PC) and anomaly detection (AD), neither the memorization of each map image nor the maintenance of up-to-date place-specific anomaly detectors are required in this FD approach. A significant challenge that is encountered when incorporating different SLAM sub-modules into FD involves dealing with the varying scales of objects that have changed (e.g., the appearance of small dangerous obstacles on the floor). To address this issue, we reconsider the bag-of-words (BoW) image representation, by exploiting its recent advances in terms of self-localization and change detection. As a key advantage, BoW image representation can be reorganized into any different scaling by simply cropping the original BoW image. Furthermore, we propose to combine different self-localization modules with strong and weak BoW features with different discriminability, and to treat inconsistency between strong and weak self-localization as an indicator of change. The efficacy of the proposed approach for FD with/without AD and/or PC was experimentally validated.

Image change detection is a fundamental problem for robotic map maintenance and long-term map learning. Local feature-based image comparison is one of the most basic schemes for addressing this problem. However, the local-feature approach encounters difficulties when the query and reference images involve different domains (e.g., time of the day, weather, season). In this paper, we address the local-feature approach from the novel perspective of object-level region features. This study is inspired by the recent success of object-level region features in cross-domain visual place recognition (CD-VPR). Unlike the previous contributions of the CD-VPR task, in the cross-domain change detection (CD-CD) tasks, we consider matching a small part (i.e., the change) of the scene and not the entire image, which is considerably more demanding. To address this issue, we explore the use of two independent object proposal techniques: supervised object proposal (e.g., YOLO) and unsupervised object proposal (e.g., BING). We combine these techniques and compute appearance features of their arbitrarily shaped objects by aggregating local features from a deep convolutional neural network (DCN). Experiments using a publicly available cross-season NCLT dataset validate the efficacy of the proposed approach.