Constructing Maps for Autonomous Robotics: An Introductory Conceptual Overview

Abstract

Mapping the environment is a powerful technique for enabling autonomy through localization and planning in robotics. This article seeks to provide a global overview of actionable map construction in robotics, outlining the basic problems, introducing techniques for overcoming them, and directing the reader toward established research covering these problem and solution domains in more detail. Multiple levels of abstraction are covered in a non-exhaustive vertical slice, starting with the fundamental problem of constructing metric occupancy grids with Simultaneous Mapping and Localization techniques. On top of these, topological meshes and semantic maps are reviewed, and a comparison is drawn between multiple representation formats. Furthermore, the datasets and metrics used in performance benchmarks are discussed, as are the challenges faced in some domains that deviate from typical laboratory conditions. Finally, recent advances in robot control without explicit map construction are touched upon.

Development of Autonomous Mobile Robot with 3DLidar Self-Localization Function Using Layout Map

“…These advancements hold promise for enhancing the efficiency and capabilities of unmanned guided vehicles across various industrial applications. SLAM technology has transcended its traditional industrial applications and found utility beyond factory settings [6,7]. For instance, iRobot's Roomba 980 cleaning robot efficiently navigates rooms using SLAM technology [8].…”