Probabilistic Topological Map Inference with Belief Propagation

Abstract

Metric Simultaneous Localization and Mapping (SLAM) prioritizes geometric accuracy of estimated robot poses and maps. However, in many real-world robot applications, such as inspection robots operating inside pipelines or other confined network environments, metric accuracy is less critical than correctly capturing the underlying topological connectivity. In this paper, we investigate back-end optimization for topological mapping/SLAM, and propose a probabilistic topological map inference algorithm. Given noisy front-end measurements, our approach explicitly models the topological map inference problem within a factor graph framework. It performs inference using belief propagation, which yields a posterior distribution over multiple plausible topological maps rather than a single estimate. We evaluate our method on topologies derived from an open-source pipeline network dataset, spanning various topology sizes and degrees of perceptual aliasing. Extensive experiments demonstrate that our algorithm infers high-quality topological maps across varying conditions.