SoMaSLAM: 2D Graph SLAM for Sparse Range Sensing with Soft Manhattan World Constraints

TL;DR

SoMaSLAM is a 2D graph SLAM algorithm for tiny robots that uses soft Manhattan constraints to improve localization accuracy with sparse range data, without strict structural assumptions.

Contribution

It introduces a novel soft Manhattan world constraint and landmark-landmark constraints into graph SLAM for better accuracy in sparse sensing environments.

Findings

Improves localization accuracy on diverse datasets.

Effectively maps sparse range data without strict structural enforcement.

Demonstrates robustness and real-world applicability.

Abstract

We propose a graph SLAM algorithm for sparse range sensing that incorporates a soft Manhattan world utilizing landmark-landmark constraints. Sparse range sensing is necessary for tiny robots that do not have the luxury of using heavy and expensive sensors. Existing SLAM methods dealing with sparse range sensing lack accuracy and accumulate drift error over time due to limited access to data points. Algorithms that cover this flaw using structural regularities, such as the Manhattan world (MW), have shortcomings when mapping real-world environments that do not coincide with the rules. We propose SoMaSLAM, a 2D graph SLAM designed for tiny robots with sparse range sensing. Our approach effectively maps sparse range data without enforcing strict structural regularities and maintains an adaptive graph. We implement the MW assumption as soft constraints, which we refer to as a soft Manhattan world. We propose novel soft landmark-landmark constraints to incorporate the soft MW into graph SLAM. Through extensive evaluation, we demonstrate that our proposed SoMaSLAM method improves localization accuracy on diverse datasets and is flexible enough to be used in the real world. We release our source code and sparse range datasets at https://SoMaSLAM.github.io/.