Incremental Loop Closure Verification by Guided Sampling

TL;DR

This paper introduces a guided sampling framework for loop closure verification in SLAM, prioritizing plausible hypotheses to improve precision-recall balance and efficiency in geometric verification.

Contribution

It proposes a novel multi-model hypothesize-and-verify framework with guided sampling to verify loop closures more effectively than uniform methods.

Findings

Achieves better precision-recall trade-off in loop closure detection.

Operates in constant time by planned hypothesis verification.

Effective despite many false positives in constraints and hypotheses.

Abstract

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.