Graph Theoretical Outlier Rejection for 4D Radar Registration in Feature-Poor Environments

2026-04-16

AI Summary

This work addresses the challenge of scan registration for 4D imaging radar in feature-sparse environments such as open-pit mines, where sparse point clouds, noise, and multipath effects severely degrade performance. To enhance robustness and accuracy, the authors propose a novel registration method that integrates graph-theoretic pairwise consistency maximization (PCM) into the iterative closest point (ICP) framework to effectively reject outlier correspondences. Furthermore, they introduce a distance-invariant scoring function that incorporates anisotropic detection uncertainties and employ a greedy clique search to efficiently identify the maximum clique in the consistency graph. Evaluated on an open-pit mine dataset, the proposed approach reduces relative pose errors by 29.6% at the 1-meter segment and up to 55% at the 100-meter segment compared to a GICP baseline without PCM, demonstrating significantly improved registration robustness and precision.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Automotive 4D imaging radar is well suited for operation in dusty and low-visibility environments, but scan registration remains challenging due to scan sparsity and spurious detections caused by noise and multipath reflections. This difficulty is compounded in feature-poor open-pit mines, where the lack of distinctive landmarks reduces correspondence reliability. We integrate graph-based pairwise consistency maximization (PCM) as an outlier rejection step within the iterative closest points (ICP) loop. We propose a radar-adapted pairwise distance-invariant scoring function for graph-based (PCM) that incorporates anisotropic, per-detection uncertainty derived from a radar measurement model. The consistency maximization problem is approximated with a greedy heuristic that finds a large clique in the pairwise consistency graph. The refined correspondence set improves robustness when the initial association set is heavily contaminated. We evaluate a standard Euclidean distance residual and our uncertainty-aware residual on an open-pit mine dataset collected with a 4D imaging radar. Compared to the generalized ICP (GICP) baseline without PCM, our method reduces segment relative position error (RPE) by 29.6% on 1 m segments and by up to 55% on 100 m segments. The presented method is intended for integration into localization pipelines and is suitable for online use due to the greedy heuristic in graph-based (PCM).

Problem

• 4D radar
• scan registration
• outlier rejection
• feature-poor environments
• spurious detections

Innovation

• graph-based outlier rejection
• 4D radar registration
• pairwise consistency maximization
• anisotropic uncertainty
• greedy clique heuristic