aiqbench analysis
PROBE-X addresses a critical bottleneck in autonomous navigation by enabling accurate place recognition with a single LiDAR scan, even when the sensor's field of view differs significantly from stored maps. By utilizing learning-free probabilistic occupancy representations and angular masks to handle heading uncertainty, this method improves recall metrics without relying on large-scale training data or multi-scan accumulation. This advancement is particularly relevant for robotics applications requiring robust localization in dynamic environments where computational efficiency and data scarcity are constraints.
PROBE-X: Learning-Free Cross-FOV Place Recognition
arXiv:2607.06782v2 Announce Type: replace Abstract: Under field-of-view (FOV) mismatch, pooling LiDAR features over unequal angular support can distort compact retrieval keys and exclude correct matches before geometric verification. We present PROBE-X, a learning-free method for single-scan cross-FOV place recognition. Building on PROBE's probabilistic occupancy representation, which models translation uncertainty, PROBE-X constructs ring-mean retrieval keys using angular masks conditioned on up to four fixed hypotheses for the unknown relative heading. Candidate lists are merged by maximum cosine similarity. Each retained candidate is aligned under the hypotheses that retrieved it and assigned a geometric score over the angular overlap at the refined heading. Across controlled-FOV and heterogeneous-LiDAR experiments, PROBE-X achieves higher Recall@1 and PR-AUC than the evaluated single-scan baselines in most settings with limited-FOV queries and panoramic database scans. On the controlled asymmetric-FOV sweep, single-scan PROBE-X also outperforms the baselines evaluated with up to ten accumulated scans on both metrics. In two ablation settings, removing the angular masks from retrieval-key construction reduces candidate coverage and Recall@1, even when alignment and scoring still account for angular support. Project page: https://sites.google.com/view/probex-pr
Abstract:Under field-of-view (FOV) mismatch, pooling LiDAR features over unequal angular support can distort compact retrieval keys and exclude correct matches before geometric verification. We present PROBE-X, a learning-free method for single-scan cross-FOV place recognition. Building on PROBE's probabilistic occupancy representation, which models translation uncertainty, PROBE-X constructs ring-mean retrieval keys using angular masks conditioned on up to four fixed hypotheses for the unknown relative heading. Candidate lists are merged by maximum cosine similarity. Each retained candidate is aligned under the hypotheses that retrieved it and assigned a geometric score over the angular overlap at the refined heading. Across controlled-FOV and heterogeneous-LiDAR experiments, PROBE-X achieves higher Recall@1 and PR-AUC than the evaluated single-scan baselines in most settings with limited-FOV queries and panoramic database scans. On the controlled asymmetric-FOV sweep, single-scan PROBE-X also outperforms the baselines evaluated with up to ten accumulated scans on both metrics. In two ablation settings, removing the angular masks from retrieval-key construction reduces candidate coverage and Recall@1, even when alignment and scoring still account for angular support. Project page: this https URL
Substantially revised and retitled. Focused on single-scan cross-FOV place recognition, with updated retrieval design and evaluation. The localization backend of v1 is not included. 8 pages, 5 figures, 5 tables
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2607.06782 [cs.RO]
(or arXiv:2607.06782v2 [cs.RO] for this version)
Full text under the publisher's open licence · 220 words
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