The Graph SLAM Algorithm with Applications to Large-Scale Mapping of Urban Structures

TLDR

GraphSLAM builds on recent optimization‑based SLAM research. The paper introduces GraphSLAM, a unifying algorithm for offline SLAM. GraphSLAM models the SLAM posterior as a graphical network, reduces it via variable elimination, and applies a greedy data‑association algorithm to solve the resulting lower‑dimensional optimization problem. GraphSLAM produces maps with over 108 features and demonstrates effective SLAM in urban settings with intermittent GPS.

Abstract

This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem. GraphSLAM is closely related to a recent sequence of research papers on applying optimization techniques to SLAM problems. It transforms the SLAM posterior into a graphical network, representing the log-likelihood of the data. It then reduces this graph using variable elimination techniques, arriving at a lower-dimensional problems that is then solved using conventional optimization techniques. As a result, GraphSLAM can generate maps with 108 or more features. The paper discusses a greedy algorithm for data association, and presents results for SLAM in urban environments with occasional GPS measurements.