  Recognizing a previously visited place, also known as place recognition (or
loop closure detection) is the key towards fully autonomous mobile robots and
self-driving vehicle navigation. Augmented with various Simultaneous
Localization and Mapping techniques (SLAM), loop closure detection allows for
incremental pose correction and can bolster efficient and accurate map
creation. However, repeated and similar scenes (perceptual aliasing) and long
term appearance changes (e.g. weather variations) are major challenges for
current place recognition algorithms. We introduce a new dataset Multisensory
Omnidirectional Long-term Place recognition (MOLP) comprising omnidirectional
intensity and disparity images. This dataset presents many of the challenges
faced by outdoor mobile robots and current place recognition algorithms. Using
MOLP dataset, we formulate the place recognition problem as a regularized
sparse convex optimization problem. We conclude that information extracted from
intensity image is superior to disparity image in isolating discriminative
features for successful long term place recognition. Furthermore, when these
discriminative features are extracted from an omnidirectional vision sensor, a
robust bidirectional loop closure detection approach is established, allowing
mobile robots to close the loop, regardless of the difference in the direction
when revisiting a place.
