A Tutorial on Graph-Based SLAM

TLDR

Building a map and simultaneously localizing within it is essential for mobile robots navigating unknown environments without external references such as GPS, and the SLAM problem—often formulated as a graph whose nodes represent robot poses and edges encode constraints from observations or movements—has been a popular research topic for two decades, with the map computed by finding the spatial configuration of nodes most consistent with the measurements modeled by the edges. This tutorial introduces the graph‑based SLAM problem and aims to enable readers to implement the proposed methods from scratch, including a discussion of a state‑of‑the‑art solution based on least‑squares error minimization. The discussed solution employs least‑squares error minimization that exploits the structure of SLAM problems during optimization.

Abstract

Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.