Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

TL;DR

This paper introduces a graph-based multi-sensor fusion method for accurate, real-time localization of construction robots, improving robustness and consistency in complex, large-scale environments.

Contribution

It presents a novel dual-graph approach that combines filtering and smoothing for robust, high-rate state estimation with asynchronous sensor integration and dropout handling.

Findings

Achieved consistent localization during sensor dropout.

Validated on real excavators in operational environments.

Enhanced global positioning accuracy for large-scale construction robots.

Abstract

Enabling autonomous operation of large-scale construction machines, such as excavators, can bring key benefits for human safety and operational opportunities for applications in dangerous and hazardous environments. To facilitate robot autonomy, robust and accurate state-estimation remains a core component to enable these machines for operation in a diverse set of complex environments. In this work, a method for multi-modal sensor fusion for robot state-estimation and localization is presented, enabling operation of construction robots in real-world scenarios. The proposed approach presents a graph-based prediction-update loop that combines the benefits of filtering and smoothing in order to provide consistent state estimates at high update rate, while maintaining accurate global localization for large-scale earth-moving excavators. Furthermore, the proposed approach enables a flexible integration of asynchronous sensor measurements and provides consistent pose estimates even during phases of sensor dropout. For this purpose, a dual-graph design for switching between two distinct optimization problems is proposed, directly addressing temporary failure and the subsequent return of global position estimates. The proposed approach is implemented on-board two Menzi Muck walking excavators and validated during real-world tests conducted in representative operational environments.