DCL-Sparse: Distributed Relative Localization in Sparse Graphs
Atharva Sagale, Tohid Kargar Tasooji, Ramviyas Parasuraman
AI summary
Problem
Existing distributed cooperative localization methods struggle with convergence and accuracy in GPS-denied environments when sensing graphs are sparse or noisy, leading to geometric non-rigidity and flipping ambiguities.
Approach
The DCL-Sparse framework introduces a distributed protocol that infers virtual shadow 1-hop edges between non-adjacent robots via their common neighbors, using geometric bounds to stabilize localization without requiring dense connectivity or centralized computation.
Key results
- Eliminates pose flipping in non-rigid sensing graphs
- Reduces localization error by up to 93% over state-of-the-art
- Establishes theoretical gain bounds ensuring stability under noise
- Validated through extensive simulations and real-world experiments
Why it matters
Enables reliable, large-scale multi-robot coordination in GPS-denied, bandwidth-constrained environments like search-and-rescue or underground exploration.
Abstract
This paper presents a novel approach to range- based distributed cooperative localization (DCL) for robot swarms in GPS-denied environments, relying solely on inter- robot range measurements, specifically addressing the limita- tions of current methods in noisy and sparse settings where the geometric non-rigidity of the sensing graph creates flipping (suboptimal) effects in the localization outcomes. We propose a robust multilayered localization framework (DCL-Sparse) that utilizes distributed 1-hop shadow edges (S1-Edge) to address the non-rigidity problem and improve localization convergence in sparse and noisy sensing graphs. Our approach leverages the advantages of distributed localization methods, enhancing scalability and adaptability in large robot networks. We establish theoretical conditions for the new S1-Edge that ensure solutions exist even in the presence of noise, thereby validating the effectiveness of the new shadow edge localization. Extensive simulation and real-world experiments confirm the superior performance of our method compared to state-of-the- art techniques, resulting in a reduction of up to 93% in the localization error in DCL. These experiments demonstrate sub- stantial improvements in localization accuracy and robustness to sparse graphs. DCL-Sparse increases the localizability of large multi-robot and sensor networks, offering a powerful tool for high-performance and reliable operations in challenging large-scale environments.