arXiv Computer Vision
HuMemSLAM: Efficient Human-Inspired Semantic Place Recognition for Robust Visual SLAM
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arXiv Computer Vision
Sep 21
EventGeM: Global-to-Local Feature Matching for Event-Based Visual Place Recognition
EventGeM introduces a global‑to‑local feature fusion pipeline for event‑based visual place recognition, combining whole‑image feature detection with 2D homography‑based re‑ranking via RANSAC. It adds a regional generalized mean (GeM) pooling layer that learns to extract the most relevant spatial features from event streams, producing a compact global descriptor trained on the NYC‑Event‑VPR dataset. The method demonstrates significant improvements in viewpoint‑robust localization, achieving 7–43 percentage point gains in Recall@1 over the strongest baseline and real‑time performance on a robotic platform.
arXiv Computer Vision
Sep 22
Desc++: Efficient Descriptor Enhancement for Data Association in Existing Visual SLAM Systems
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 Computer Vision
Oct 27
VPR-Space: A Benchmark for Visual Place Recognition in Space Analog Domains
arXiv:2610.35642v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-o...
arXiv Computer Vision
Oct 16
RoboAtlas: Contextual Active SLAM
RoboAtlas is a contextual Active SLAM framework that balances geometric exploration with semantic reasoning through a scalable 3D semantic mapping system called OpenRoboVox. It uses frontier exploration, global semantic‑map reasoning, and egocentric VLM‑based reasoning guided by a contextual multi‑armed bandit to shift from exploration to semantically guided navigation as scene understanding improves. In simulation and on a Unitree Go2 robot, RoboAtlas achieved a 100% task success rate in environments over 1800 m² with ~30k semantic instances, and outperformed prior baselines on the GOAT‑Bench "Val Unseen" benchmark, reaching 90.6% SR with GPT‑4o and 88.8% SR with Qwen2.5‑VL‑7B.
arXiv Computer Vision
Sep 21
Multi-viewpoint Geo-localization with Event Cameras
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.
arXiv Computer Vision
Sep 17
RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
arXiv:2607.20116v2 Announce Type: replace Abstract: Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, diff...