Weighted Group-K Consistent Set Maximization for Outlier Rejection of Azimuth-Elevation Measurements
Kalliyan Velasco, T.W. McLain, Joshua Mangelson
AI summary
Problem
Reliable robotic localization is hindered by noisy bearing measurements, particularly in underwater environments where pairwise consistency is insufficient. Existing group-k consistent set maximization methods lack dedicated bearing metrics and suffer from high computational costs that limit real-time deployment.
Approach
The authors extend group-k consistent set maximization to weighted k-uniform hypergraphs and use replicator dynamics to rapidly identify the densest consistent subsets of azimuth-elevation measurements, followed by a fitness-guided refinement step to extract valid cliques.
Key results
- Introduction of a k=3 azimuth-elevation consistency metric for static landmarks
- Development of a replicator-dynamics-based algorithm for weighted hypergraph clique finding
- Significant computational speedup over heuristic unweighted GkCM with comparable accuracy in simulations
- Successful real-world outlier rejection and localization on an autonomous surface vessel in a multipath-prone marina
Why it matters
Enables robust, efficient localization for underwater robotics and bearing-based perception systems by providing a fast, reliable method for rejecting acoustic outliers in real-time.
Abstract
Reliable localization in robotics requires robust handling of sensor outliers, particularly in environments where acoustic or bearing measurements are noisy. We propose a replicator-dynamics-based approach for weighted group-k consistent set maximization (rGkCM) to identify the densest subsets of mutually consistent measurements in hypergraphs. To complement existing range-based consistency metrics, we introduce a k = 3 azimuth-elevation consistency check for bearing measurements to static landmarks. Our method efficiently identifies cliques in weighted k-uniform hypergraphs, leveraging the fitness of nodes to guide both pruning and recovery. We evaluate rGkCM on simulated trajectories with varying outlier levels and demonstrate significant computational speedup over the heuristic unweighted GkCM (uGkCM) method while maintaining comparable accuracy. Finally, we validate the approach on a WAM-V autonomous surface vessel equipped with an acoustic beacon and GNSS ground truth, showing effective outlier rejection in a shallow, multipath-prone marina. Results indicate that rGkCM enables robust and efficient outlier rejection for real-world bearing-based localization tasks.