Precise Robot Localization in Architectural 3D Plans

TL;DR

This paper introduces a localization system for mobile robots that combines local referencing, a robust outlier detector, and sensor fusion to achieve highly accurate positioning within complex building environments, outperforming traditional methods.

Contribution

The paper presents a novel localization approach that integrates local referencing with an image-based outlier detector and LiDAR data fusion for improved accuracy in building models.

Findings

Reduces localization error by at least 30% compared to traditional ICP-based alignment.

Effectively rejects outliers from clutter, dynamic objects, and sensor failures.

Demonstrates robust performance in real-world construction site environments.

Abstract

This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel image-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. We evaluate the proposed approach on a mobile robot in a challenging real world building construction site. It consistently outperforms the traditional ICP-based alingment, reducing localization error by at least 30%.