The study evaluates eleven feature detector–matcher combinations for lunar visual SLAM using synthetic, lunar-testbed, and terrestrial-analogue datasets. SuperPoint pairings outperform SIFT with ratio-tested FLANN in a rotation-only metric on lunar testbeds, but SIFT excels when rotation and translation are jointly scored on synthetic data. In stereo VO, SuperPoint maintains tracking across all datasets, while classical detectors are more sensitive to lighting conditions, and the fastest learned combination is SuperPoint with LightGlue.
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...
Precise rover localization is a prerequisite for autonomous lunar exploration, yet the absence of Global Navigation Satellite System (GNSS) signals and the cumulative drift of local localization methods severely constrain long-range missions. Cross-view localization provides a promising drift-free global solution by matching rover-view and satellite-view imagery.
Deep-learning features excel in visual matching, yet their practical value in tightly coupled visual-inertial SLAM (VI-SLAM) remains insufficiently characterized. We present DL-VINS-Factory, a unified framework that integrates learned feature extractors (ALIKED, RaCo, SuperPoint, XFeat) with either Lucas--Kanade (LK) optical-flow tracking or LightGlue (LG) descriptor matching.
Stereo visual SLAM systems built on local descriptors suffer from semantic ambiguity, instance-level confusion, and independently moving objects, each corrupting data association and accumulating as t...
Know-Your-Scene (KYS)-SLAM extends ORB‑SLAM3 by replacing binary feature rejection with continuous correspondence modulation based on semantic, panoptic, and motion priors. Each keypoint is augmented with hierarchical compatibility scores that down‑weight features on independently moving objects while preserving static structure, using a training‑free depth‑aware ego‑motion model and self‑calibrating thresholds. Across 21 stereo sequences, KYS‑SLAM achieves a 17.4% ATE RMSE reduction on outdoor KITTI, 27.7% on indoor EuRoC, and significant improvements on dynamic and synthetic datasets without per‑sequence tuning.
arXiv:2610.07110v1 Announce Type: new Abstract: High-quality 3D reconstruction of lunar terrain from sparse rover images is indispensable for autonomous lunar exploration, but remains challenging bec...
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...
DAPEVO is a learned visual odometry system that independently estimates image and event correspondences at shared patch locations and fuses their correlation evidence before motion refinement. It maintains image and event descriptors for each tracked patch, using a learned scalar gate to combine modality-specific correlation embeddings for each patch–frame edge, followed by a shared recurrent refinement and bundle‑adjustment update. The method supports event‑only observations and modality‑aware keyframe culling, achieving low trajectory error even when RGB frames are sparse or degraded, outperforming DPVO, RAMP‑VO, and event‑only DEVO on UZH‑FPV and TartanEvent datasets.
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.
The study evaluates whether dynamic-point filtering improves ORB‑SLAM2 performance in low‑texture indoor scenes by systematically varying texture and scene dynamics in synthetic sequences. Using stereo, RGB‑D, and ground‑truth data, the authors compare ORB‑SLAM2 without filtering, with an optical‑flow/epipolar filter (FLOW), and with a multi‑view depth‑consistency filter (GEOM), measuring trajectory error, tracking completeness, and static‑feature retention. Results show filtering mainly helps in highly dynamic environments; at the lowest texture level it reduces tracking completeness, and ORB‑SLAM2 fails to initialize in static low‑texture scenes, with GEOM discarding more static keypoints than FLOW. "whyItMatters":"The findings challenge the assumption that dynamic‑point filtering always benefits SLAM, highlighting its limited effectiveness in low‑texture settings and guiding future filter design."
arXiv:2609.21212v1 Announce Type: cross Abstract: Learned navigation policies typically consume observations as a temporally ordered history, with positional encodings tying each observation to when...
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...