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Recognizing a previously visited place, also known as place recognition (orloop closure detection) is the key towards fully autonomous mobile robots andself-driving vehicle navigation. Augmented with various SimultaneousLocalization and Mapping techniques (SLAM), loop closure detection allows forincremental pose correction and can bolster efficient and accurate mapcreation. However, repeated and similar scenes (perceptual aliasing) and longterm appearance changes (e.g. weather variations) are major challenges forcurrent place recognition algorithms. We introduce a new dataset MultisensoryOmnidirectional Long-term Place recognition (MOLP) comprising omnidirectionalintensity and disparity images. This dataset presents many of the challengesfaced by outdoor mobile robots and current place recognition algorithms. UsingMOLP dataset, we formulate the place recognition problem as a regularizedsparse convex optimization problem. We conclude that information extracted fromintensity image is superior to disparity image in isolating discriminativefeatures for successful long term place recognition. Furthermore, when thesediscriminative features are extracted from an omnidirectional vision sensor, arobust bidirectional loop closure detection approach is established, allowingmobile robots to close the loop, regardless of the difference in the directionwhen revisiting a place.

Multisensory Omni-directional Long-term Place Recognition: Benchmark Dataset and Analysis