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Following a Unique Path: A Fast Certifier Applied to Outlier-Robust Pose Registration

Summary

Certifiable methods provide global optimality guarantees for non-convex problems through convex semidefinite programming (SDP) relaxations. The fastest such methods obtain a candidate solution with a local solver, then certify it via efficient linear algebra. However, a degeneracy in the relaxation blocks this pipeline for many robotics problems. This paper introduces the Central-Path Certifier (CP-Cert), which starts from a candidate and seeks a nearby region of the feasible space—the central path—where a certificate is easy to obtain, while exploiting indirect linear algebra, sparsity, and parallelism. Applied to matrix-weighted pose registration and pointcloud data association, CP-Cert runs up to three orders of magnitude faster than state-of-the-art direct solvers in simulation, and i…

SourcearXiv RoboticsAuthor: Connor Holmes, Abhishek Goudar, Timothy D. Barfoot
Following a Unique Path: A Fast Certifier Applied to Outlier-Robust Pose Registration
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[Submitted on 2 Sep 2026]

Title:Following a Unique Path: A Fast Certifier Applied to Outlier-Robust Pose Registration

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Abstract:Certifiable methods have arisen as a means to guarantee global optimality of solutions to non-convex problems using convex semidefinite programming (SDP) relaxations. The most performant of these methods use a local solver to obtain the candidate solution, and then certify its optimality using efficient linear algebra techniques. However, for many problems of interest in robotics, this local-solve-then-certify approach is impeded by a form of degeneracy in the relaxation, leaving a costly optimization of the relaxation as the only recourse. In this paper, we introduce our Central-Path Certifier (CP-Cert), a certifiable method explicitly tailored to certify candidate optima to problems that exhibit this form of degeneracy. Using a candidate as a starting point, our approach seeks a nearby region of the feasible space -- known as the central path -- where a valid certificate can be readily obtained. The approach is kept efficient by exploiting indirect linear algebra techniques, problem sparsity, and parallelism. We apply CP-Cert to both matrix-weighted pose registration and pointcloud data association, whose novel SDP relaxation is of independent interest. On simulated examples, we explore the properties of this novel relaxation and show that CP-Cert is fast and scalable, achieving runtimes that are up to three orders of magnitude faster than state-of-the-art direct solvers. Finally, we combine these contributions into a certifiable, outlier-robust pose-estimation pipeline, which we apply to real-world data.

Subjects:

Robotics (cs.RO)

Cite as: arXiv:2609.03222 [cs.RO]

(or arXiv:2609.03222v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.03222

arXiv-issued DOI via DataCite (pending registration)

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From: Connor Holmes [view email] [v1] Wed, 2 Sep 2026 23:39:56 UTC (14,962 KB)

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Key points

  • Introduces CP-Cert, a certifiable method tailored to problems where relaxation degeneracy prevents standard local-solve-then-certify pipelines.
  • Finds a certificate near the candidate on the central path, using indirect linear algebra, sparsity, and parallelism for efficiency.
  • Demonstrates speedups of up to three orders of magnitude over state-of-the-art direct solvers on matrix-weighted pose registration and pointcloud data association.
  • Combines CP-Cert with local estimation into a certifiable, outlier-robust real-world pose-estimation pipeline.

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