Range-Aided Pose-Graph-Based SLAM: Applications of Deployable Ranging Beacons for Unknown Environment Exploration

Simultaneous Localization and Mapping (SLAM) is a critical part of robotic exploration in unknown environments. SLAM over large scales typically presents challenges with limiting drift and requires loop closures to correct accumulated errors. However, real-time loop closure detection can be limited by high computational cost and susceptibility to outliers, especially in perceptually challenging, unknown, and large-scale environments. One solution to these challenges is to deploy ranging beacons from a robot and to utilize them for unambiguous place recognition. In this letter, we develop an architecture to take advantage of deployable ranging beacons in pose-graph SLAM. First, we highlight the inherent ambiguity in range measurements for sparsely deployed ranging beacons by analyzing the challenges in applying conventional methods of processing range measurements for localization. To both handle this ambiguity and address the challenges of loop closure for large-scale, perceptually challenging environments, we propose range-aided loop closure (RA-LC), an algorithm that combines beacon-based place recognition with geometric loop closures. We implement RA-LC in our hardware system and demonstrate the efficiency of RA-LC in field tests. Our results show that RA-LC can achieve equivalent accuracy of geometric-only lidar-based loop closure with significantly lower computational cost (95% reduction) and eight-times fewer outliers.

Automated summary

This study proposes an algorithm that combines deployable ranging beacons from robots with geometric loop closures for SLAM, aiming to address the challenges of loop closure in large-scale environments. Through field tests, the algorithm has been shown to significantly reduce computational cost and outliers while maintaining equivalent accuracy to lidar-based loop closures.