arXiv:2608.28891v1 Announce Type: new Abstract: Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be ma...
The paper presents MegaEvent, an event‑based visual place recognition system that remains robust to viewpoint changes. By converting five large‑scale geo‑tagged datasets into synthetic event streams and fine‑tuning a vision transformer with a multi‑loss function, MegaEvent achieves an average Recall@1 of 82% on three event‑based localization datasets, outperforming existing methods by 20 recall points. The authors also introduce the Springfield‑Event‑VPR dataset, a 3.7 km walking route recorded in three camera orientations, where MegaEvent surpasses the strongest baseline by 9 recall points.
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.
arXiv:2607.11099v2 Announce Type: replace-cross Abstract: Reliable visual data association is fundamental to visual SLAM (V-SLAM), as it directly determines the quality of the camera pose estimation...
arXiv:2609.17168v1 Announce Type: cross Abstract: Autonomous systems require reliable place recognition for efficient and effective simultaneous localisation and mapping (SLAM). Traditional geometric...
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.
FreeLoc is an online RGB-based floorplan localization framework that treats the floorplan as a directly queryable geometric map. It uses efficient online geometric querying and diffusion-aided refinement to retrieve pose anchors and refine them into accurate continuous pose estimates. For sequential localization, it constructs online likelihoods from coarse-sampled candidates and refined hypotheses, enabling histogram-filter-based temporal fusion without offline databases.
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.
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.
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.
The paper introduces CalfVO, a monocular visual odometry system that operates without camera intrinsics, test‑time optimization, bundle adjustment, or loop closure. Using a transformer, it predicts relative poses with separate rotation and translation confidences over overlapping image windows, then aggregates these predictions via a confidence‑weighted module to produce a single trajectory. CalfVO achieves the highest accuracy among calibration‑free methods across five benchmarks and runs at 53 FPS, outperforming all baselines.
arXiv:2608.29003v1 Announce Type: cross Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...