  Change detection, i.e., anomaly detection from local maps built by a mobile
robot at multiple different times, is a challenging problem to solve in
practice. Most previous work either cannot be applied to scenarios where the
size of the map collection is large, or simply assumed that the robot
self-location is globally known. In this paper, we tackle the problem of
simultaneous self-localization and change detection, by reformulating the
problem as a map retrieval problem, and propose a local map descriptor with a
compressed bag-of-words (BoW) structure as a scalable solution. We make the
following contributions. (1) To enable a direct comparison of the spatial
layout of visual features between different local maps, the origin of the local
map coordinate (termed "viewpoint") is planned by scene parsing and determined
by our "viewpoint planner" to be invariant against small variations in
self-location and changes, aiming at providing similar viewpoints for similar
scenes (i.e., the relevant map pair). (2) We extend the BoW model to enable the
use of not only the appearance (e.g., polestar) but also the spatial layout
(e.g., spatial pyramid) of visual features with respect to the planned
viewpoint. The key observation is that the planned viewpoint (i.e., the origin
of local map coordinate) acts as a pseudo viewpoint that is usually required by
spatial BoW (e.g., SPM) and also by anomaly detection (e.g., NN-d, LOF). (3)
Experimental results on a challenging "loop-closing" scenario show that the
proposed method outperforms previous BoW methods in self-localization, and
furthermore, that the use of both appearance and pose information in change
detection produces better results than the use of either information alone.
