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Visual place recognition (VPR), a fundamental task in computer vision androbotics, is the problem of identifying a place mainly based on visualinformation. Viewpoint and appearance changes, such as due to weather andseasonal variations, make this task challenging. Currently, there is nouniversal VPR technique that can work in all types of environments, on avariety of robotic platforms, and under a wide range of viewpoint andappearance changes. Recent work has shown the potential of combining differentVPR methods intelligently by evaluating complementarity for some specific VPRdatasets to achieve better performance. This, however, requires ground truthinformation (correct matches) which is not available when a robot is deployedin a real-world scenario. Moreover, running multiple VPR techniques in parallelmay be prohibitive for resource-constrained embedded platforms. To overcomethese limitations, this paper presents a probabilistic complementarity basedswitching VPR system, SwitchHit. Our proposed system consists of multiple VPRtechniques, however, it does not simply run all techniques at once, ratherpredicts the probability of correct match for an incoming query image anddynamically switches to another complementary technique if the probability ofcorrectly matching the query is below a certain threshold. This innovative useof multiple VPR techniques allow our system to be more efficient and robustthan other combined VPR approaches employing brute force and running multipleVPR techniques at once. Thus making it more suitable for resource constrainedembedded systems and achieving an overall superior performance from what anyindividual VPR method in the system could have by achieved runningindependently.

SwitchHit: A Probabilistic, Complementarity-Based Switching System for Improved Visual Place Recognition in Changing Environments