  Loop closure detection, which is the task of identifying locations revisited
by a robot in a sequence of odometry and perceptual observations, is typically
formulated as a visual place recognition (VPR) task. However, even
state-of-the-art VPR techniques generate a considerable number of false
positives as a result of confusing visual features and perceptual aliasing. In
this paper, we propose a robust incremental framework for loop closure
detection, termed incremental loop closure verification. Our approach
reformulates the problem of loop closure detection as an instance of a
multi-model hypothesize-and-verify framework, in which multiple loop closure
hypotheses are generated and verified in terms of the consistency between loop
closure hypotheses and VPR constraints at multiple viewpoints along the robot's
trajectory. Furthermore, we consider the general incremental setting of loop
closure detection, in which the system must update both the set of VPR
constraints and that of loop closure hypotheses when new constraints or
hypotheses arrive during robot navigation. Experimental results using a stereo
SLAM system and DCNN features and visual odometry validate effectiveness of the
proposed approach.
