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Loop closure detection, which is the task of identifying locations revisitedby a robot in a sequence of odometry and perceptual observations, is typicallyformulated as a visual place recognition (VPR) task. However, evenstate-of-the-art VPR techniques generate a considerable number of falsepositives as a result of confusing visual features and perceptual aliasing. Inthis paper, we propose a robust incremental framework for loop closuredetection, termed incremental loop closure verification. Our approachreformulates the problem of loop closure detection as an instance of amulti-model hypothesize-and-verify framework, in which multiple loop closurehypotheses are generated and verified in terms of the consistency between loopclosure hypotheses and VPR constraints at multiple viewpoints along the robot'strajectory. Furthermore, we consider the general incremental setting of loopclosure detection, in which the system must update both the set of VPRconstraints and that of loop closure hypotheses when new constraints orhypotheses arrive during robot navigation. Experimental results using a stereoSLAM system and DCNN features and visual odometry validate effectiveness of theproposed approach.

Multi-Model Hypothesize-and-Verify Approach for Incremental Loop Closure Verification