  Visual place recognition (VPR), a fundamental task in computer vision and
robotics, is the problem of identifying a place mainly based on visual
information. Viewpoint and appearance changes, such as due to weather and
seasonal variations, make this task challenging. Currently, there is no
universal VPR technique that can work in all types of environments, on a
variety of robotic platforms, and under a wide range of viewpoint and
appearance changes. Recent work has shown the potential of combining different
VPR methods intelligently by evaluating complementarity for some specific VPR
datasets to achieve better performance. This, however, requires ground truth
information (correct matches) which is not available when a robot is deployed
in a real-world scenario. Moreover, running multiple VPR techniques in parallel
may be prohibitive for resource-constrained embedded platforms. To overcome
these limitations, this paper presents a probabilistic complementarity based
switching VPR system, SwitchHit. Our proposed system consists of multiple VPR
techniques, however, it does not simply run all techniques at once, rather
predicts the probability of correct match for an incoming query image and
dynamically switches to another complementary technique if the probability of
correctly matching the query is below a certain threshold. This innovative use
of multiple VPR techniques allow our system to be more efficient and robust
than other combined VPR approaches employing brute force and running multiple
VPR techniques at once. Thus making it more suitable for resource constrained
embedded systems and achieving an overall superior performance from what any
individual VPR method in the system could have by achieved running
independently.
