Learning Which Correspondences to Trust: Confidence-Weighted Event-Camera Localization in LiDAR Maps

Abstract:Localizing an event camera against a pre-built LiDAR map can be cast as dense optical-flow estimation between a rendered depth view and an event image, followed by a Perspective-n-Point (PnP) solver over the induced 3D-2D correspondences. Existing pipelines rely on geometric consensus during pose estimation, but do not explicitly model the reliability or pose informativeness, i.e., how strongly a correspondence constrains the camera pose, of individual correspondences. We show that the natural way to learn it -- using the per-correspondence error to constrain the learning of confidence -- suffers from a depth-dependent bias: small pixel errors reside predominantly at large depths and do not lead to high pose informativeness. Instead, in our method (CELL), we learn a per-correspondence confidence end-to-end through the pose, using a differentiable probabilistic PnP whose log-partition term encourages weight configurations that yield a better-constrained pose distribution. The learned confidence is used in three ways: (i) it reweights the flow supervision in a decoupled training scheme that keeps pose gradients out of the flow/edge backbone; (ii) it drives a probabilistic correspondence selection at test time; and (iii) together with the network's edge-probability it weights a final edge-matching refinement. We further design a partial-completion depth representation that adds signal without hallucinating across large gaps. On M3ED and DSEC our full system improves over the LEAR baseline on the majority of the evaluated sequences: it reduces the median translation error by up to 26.9% and the median rotation error by up to 15.8%.

8 pages, 8 figures/tables. Submitted to IEEE ICRA 2027 (under review). Code: this https URL

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2610.11967 [cs.CV]

(or arXiv:2610.11967v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2610.11967 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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