Visual localization becomes extremely challenging in planetary-like terrains characterized by low texture, perceptual aliasing, harsh illumination, and sparse, weakly overlapping viewpoints induced by forward rover motion and unconstrained driving directions. Under these conditions, state-of-the-art image-to-image and image-to-map matching pipelines suffer significant performance degradation.
Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisition-time and imaging-platform differences between UAV and reference imagery induce substantial cross-domain appearance and viewpoint shifts, challenging robust six-degree-of-freedom (6-DoF) pose estimation.
YILDIZ-VPR is a new visual geo‑localization dataset collected by repeatedly walking across the Davutpasa campus of Yildiz Technical University. It offers dense coverage of outdoor scenes captured at various times of day, seasons, and weather conditions, featuring historical buildings, modern structures, roads, green areas, and wooded regions. Each GoPro 9 video is synchronized with GPS, gyroscope, speed, and temperature data, providing precise location labels for extracted frames.
By Serdar Yildiz, Abbas Memi\c{s}, Song\"ul Varli
arXiv:2512. 09065v2 Announce Type: replace-cross Abstract: Many indoor workspaces are quasi-static: their global geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard vision-based localization.
By Shivendra Agrawal, Jake Brawer, Ashutosh Naik, Alessandro Roncone, Bradley Hayes
Existing Gaussian-splatting-based monocular Simultaneous Localization and Mapping (SLAM) systems are either tailored to short sequences, are not real-time, or suffer from prohibitive GPU memory requirements, limiting their applicability in realistic, long-horizon scenarios. To address this, we present GLAM-SLAM, a real-time, decoupled Gaussian-splatting SLAM system designed for large-scale outdoor scenes.
Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or short-range indoor scans, or consist of synthetic data that lacks the semantic complexity of the physical world.
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
By Liyao Wang, Ruipu Wu, Haojun Xu, Lei Shi, Linjiang Huang, Si Liu
Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted to low-level vision schemes, struggling to perceive scene semantics and compositions, which limits both interpretability and applicability.
arXiv:2604.13183v4 Announce Type: replace
Abstract: Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. It...
By Hongyang Zhang, Yinhao Liu, Haitao Zhang, Zhongyi Wen, Zhenyu Kuang, Shuxian Liang, Xian-Sheng Hua
arXiv:2608.25274v1 Announce Type: new
Abstract: Fine-grained Cross-View Localization (CVL) estimates the precise position and orientation of a ground-level image by aligning it with geo-referenced ae...
By Zimin Xia, Mubariz Zaffar, Junsheng Fu, Alexandre Alahi, Julian F. P. Kooij
GTPred is a new benchmark for geo‑temporal prediction that evaluates multi‑modal large language models (MLLMs) on 370 images taken across 120 years worldwide. It assesses predictions by matching both the year and a hierarchical location sequence, and includes annotated reasoning chains to test intermediate reasoning. Experiments on 15 MLLMs show that while visual perception is strong, models still lack world knowledge and geo‑temporal reasoning, and that adding temporal data improves location inference.
By Jinnao Li, Tingzhu Chen, Changbo Wang
arXiv:2608.21276v1 Announce Type: cross
Abstract: Coastal environments contain rich, largely unexploited geometric structure capable of providing globally referenced localization cues. In this work,...
By Derek R. Benham, Joshua G. Mangelson