  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.
