Uncertainty-Aware Tightly-Coupled GPS Fused LIO-SLAM

TL;DR

This paper introduces a GPS-fused LIO-SLAM method that enhances 3D mapping accuracy for delivery robots by reducing cumulative errors and sensor drift in urban environments.

Contribution

It presents a novel GPS integration technique for tightly-coupled LIO-SLAM, improving map accuracy and robustness in large-scale urban mapping.

Findings

Outperforms existing methods in quantitative metrics

Provides more accurate and reliable 3D maps

Effectively reduces cumulative error in urban environments

Abstract

Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualitative evaluation with other existing methods.