  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 discriminativity, 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.
