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

TL;DR

This paper introduces a multi-model hypothesize-and-verify framework for incremental loop closure detection, improving robustness by verifying multiple hypotheses against VPR constraints during robot navigation.

Contribution

It presents a novel incremental loop closure verification method reformulating the problem as a multi-model hypothesize-and-verify framework, enhancing accuracy in visual place recognition.

Findings

Effective in reducing false positives in loop closure detection

Validated with stereo SLAM and DCNN features

Improves robustness in incremental navigation scenarios

Abstract

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.