Documentdetail
ID kaart
oai:arXiv.org:2409.15506
Onderwerp
Electrical Engineering and Systems... Mathematics - Optimization and Con... Mathematics - Spectral TheoryAuteur
Somisetty, Neelkamal Nagarajan, Harsha Darbha, SwaroopCategorie
Computer Science
Jaar
2024
vermelding datum
02-10-2024
Trefwoorden
robot control cuts localization cheegerBeschrijving
This paper addresses the optimization of edge-weighted networks by maximizing algebraic connectivity to enhance network robustness.
Motivated by the need for precise robot position estimation in cooperative localization and pose-graph sparsification in Simultaneous Localization and Mapping (SLAM), the algebraic connectivity maximization problem is formulated as a Mixed Integer Semi-Definite Program (MISDP), which is NP-hard.
Leveraging spectral graph theoretic methods, specifically Cheeger's inequality, this work introduces novel "Cheeger cuts" to strengthen and efficiently solve medium-scale MISDPs.
Further, a new Mixed Integer Linear Program (MILP) is developed for efficiently computing Cheeger cuts, implemented within an outer-approximation algorithm for solving the MISDP.
A greedy k-opt heuristic is also presented, producing high-quality solutions that serve as valid lower bounds for Cheeger cuts.
Comprehensive numerical analyses demonstrate the efficacy of strengthened cuts via substantial improvements in run times on synthetic and realistic robot localization datasets.
;Comment: 63rd IEEE Conference on Decision and Control (CDC)
Somisetty, Neelkamal,Nagarajan, Harsha,Darbha, Swaroop, 2024, Spectral Graph Theoretic Methods for Enhancing Network Robustness in Robot Localization
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