Implementing Robust M-Estimators with Certifiable Factor Graph Optimization

• URL: http://arxiv.org/abs/2603.20932v1
• Date: Sat, 21 Mar 2026 20:17:44 GMT
• Title: Implementing Robust M-Estimators with Certifiable Factor Graph Optimization
• Abstract summary: We develop an adaptive reweighting scheme for factor graph-based estimation in robotics.<n>We show how one can easily implement M-estimators with certifiably correct solvers for inner subproblems.
• Score: 5.293483842121928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter estimation in robotics and computer vision faces formidable challenges from both outlier contamination and nonconvex optimization landscapes. While M-estimation addresses the problem of outliers through robust loss functions, it creates severely nonconvex problems that are difficult to solve globally. Adaptive reweighting schemes provide one particularly appealing strategy for implementing M-estimation in practice: these methods solve a sequence of simpler weighted least squares (WLS) subproblems, enabling both the use of standard least squares solvers and the recovery of higher-quality estimates than simple local search. However, adaptive reweighting still crucially relies upon solving the inner WLS problems effectively, a task that remains challenging in many robotics applications due to the intrinsic nonconvexity of many common parameter spaces (e.g. rotations and poses). In this paper, we show how one can easily implement adaptively reweighted M-estimators with certifiably correct solvers for the inner WLS subproblems using only fast local optimization over smooth manifolds. Our approach exploits recent work on certifiable factor graph optimization to provide global optimality certificates for the inner WLS subproblems while seamlessly integrating into existing factor graph-based software libraries and workflows. Experimental evaluation on pose-graph optimization and landmark SLAM tasks demonstrates that our adaptively reweighted certifiable estimation approach provides higher-quality estimates than alternative local search-based methods, while scaling tractably to realistic problem sizes.

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