Open Source Robot Localization for Non-Planar Environments

TL;DR

This paper presents a novel robot localization framework that accounts for non-flat terrains by incorporating elevation and incline data, improving accuracy in complex indoor and outdoor environments.

Contribution

The study introduces a ground elevation-aware localization method using Gridmaps and Octomaps, fully integrated with Nav2, outperforming traditional 2D localization in non-planar terrains.

Findings

Achieved localization errors below 10 cm and 0.05 radians indoors.

Demonstrated robustness in outdoor and uneven terrains.

Outperformed traditional 2D SLAM algorithms in complex environments.

Abstract

The operational environments in which a mobile robot executes its missions often exhibit non-flat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional 2D localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline AMCL approach when the robot is in non-planar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 centimeters and 0.05 radians in indoor settings and less than 1.0 meters in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to 3D SLAM algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environments.