  Loop closure detection, the task of identifying locations revisited by a
robot in a sequence of odometry and perceptual observations, is typically
formulated as a combination of two subtasks: (1) bag-of-words image retrieval
and (2) post-verification using RANSAC geometric verification. The main
contribution of this study is the proposal of a novel post-verification
framework that achieves good precision recall trade-off in loop closure
detection. This study is motivated by the fact that not all loop closure
hypotheses are equally plausible (e.g., owing to mutual consistency between
loop closure constraints) and that if we have evidence that one hypothesis is
more plausible than the others, then it should be verified more frequently. We
demonstrate that the problem of loop closure detection can be viewed as an
instance of a multi-model hypothesize-and-verify framework and build guided
sampling strategies on the framework where loop closures proposed using image
retrieval are verified in a planned order (rather than in a conventional
uniform order) to operate in a constant time. Experimental results using a
stereo SLAM system confirm that the proposed strategy, the use of loop closure
constraints and robot trajectory hypotheses as a guide, achieves promising
results despite the fact that there exists a significant number of false
positive constraints and hypotheses.
