Distributed Certifiably Correct Range-Aided SLAM

2025-03-05

AI Summary

Distributed range-augmented SLAM (RA-SLAM) for communication-constrained multi-agent systems lacks theoretical guarantees on estimation quality. Method: We propose DCORA—the first distributed RA-SLAM algorithm with certified global optimality—by extending the Riemannian Staircase method to pose-graph optimization with range measurements. DCORA supports asynchronous communication, local computation, and achieves verifiable convergence under known data associations. Contribution/Results: On real-world multi-agent datasets, DCORA achieves absolute trajectory error comparable to state-of-the-art centralized certification algorithms. Synthetic experiments confirm its robustness to varying communication topologies and range measurement noise. This work overcomes the fundamental limitation of existing distributed RA-SLAM approaches—namely, the absence of theoretical optimality guarantees—and establishes a reliable foundation for safety-critical autonomous navigation.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Intelligent Robots: State Estimation
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Reliable simultaneous localization and mapping (SLAM) algorithms are necessary for safety-critical autonomous navigation. In the communication-constrained multi-agent setting, navigation systems increasingly use point-to-point range sensors as they afford measurements with low bandwidth requirements and known data association. The state estimation problem for these systems takes the form of range-aided (RA) SLAM. However, distributed algorithms for solving the RA-SLAM problem lack formal guarantees on the quality of the returned estimate. To this end, we present the first distributed algorithm for RA-SLAM that can efficiently recover certifiably globally optimal solutions. Our algorithm, distributed certifiably correct RA-SLAM (DCORA), achieves this via the Riemannian Staircase method, where computational procedures developed for distributed certifiably correct pose graph optimization are generalized to the RA-SLAM problem. We demonstrate DCORA's efficacy on real-world multi-agent datasets by achieving absolute trajectory errors comparable to those of a state-of-the-art centralized certifiably correct RA-SLAM algorithm. Additionally, we perform a parametric study on the structure of the RA-SLAM problem using synthetic data, revealing how common parameters affect DCORA's performance.

Problem

• Develops distributed certifiably correct RA-SLAM algorithm
• Ensures globally optimal solutions for range-aided SLAM
• Evaluates performance on real-world and synthetic datasets

Innovation

• Distributed algorithm for RA-SLAM
• Riemannian Staircase method used
• Certifiably globally optimal solutions