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This paper tackles the problem of scan registration for 4D radar in challenging, feature-poor environments like open-pit mines by integrating graph-based pairwise consistency maximization (PCM) within the ICP loop. They introduce a novel radar-adapted pairwise distance-invariant scoring function that incorporates anisotropic, per-detection uncertainty derived from a radar measurement model. Experiments on a real-world open-pit mine dataset demonstrate a reduction in segment relative position error (RPE) by up to 55% compared to GICP, highlighting the robustness of the approach in environments with sparse and noisy radar data.
By intelligently pruning noisy radar detections, this method achieves up to 55% better scan registration accuracy in feature-poor environments where standard ICP struggles.
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).