Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adverse weather, illumination, blur, and sensor artifacts. Controlled corruptions are attracti...
arXiv:2609.14634v1 Announce Type: new Abstract: Recently, 3D Gaussian Splatting SLAM (3DGS-SLAM) has gained significant momentum in simultaneous localization and 3DGS scene reconstruction. In real-wo...
SLAM Adversarial Lab (SAL) is a modular framework designed to evaluate visual SLAM systems under adverse conditions such as fog, rain, camera, and video transport perturbations. It transforms existing datasets into adversarial versions by applying perturbations with severity levels expressed in real‑world units (e.g., meters for fog visibility). SAL’s extensible architecture separates datasets, perturbations, and SLAM algorithms via common interfaces, allowing users to add new components without rewriting integration code, and includes a search procedure to identify the severity at which a SLAM system fails.
arXiv:2508.13488v2 Announce Type: replace-cross Abstract: Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical k...
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
arXiv:2609.15795v1 Announce Type: new Abstract: Streaming geometric foundation models are emerging as a compelling alternative to SLAM systems. Yet this streaming nature introduces a fundamental issu...
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.
arXiv:2606. 29237v1 Announce Type: cross Abstract: Robust robot autonomy depends on scene representations that remain stable enough to support localization, navigation, and downstream decision making in dynamic environments.
End-to-end multimodal driving has progressed rapidly by fusing camera and LiDAR streams. Existing pipelines remain fragile under asymmetric sensor degradation, where either an entire modality or only...
arXiv:2608.24544v1 Announce Type: new Abstract: Many feature-based visual-inertial odometry (VIO) systems rely on sparse feature tracking, whose accuracy and robustness directly affect state estimati...
arXiv:2607. 11686v1 Announce Type: cross Abstract: Low-cost unmanned ground vehicles are often used in indoor places like warehouses, inspection corridors, and farm rows, where painted floor lines guide the robot.
The paper introduces Variance‑Guided Spatial Attention Fusion (VG‑SAF), a method for robust end‑to‑end driving that fuses camera and LiDAR data while handling asymmetric sensor degradation. VG‑SAF uses a physically grounded augmentor to generate dense reliability masks, modality‑specific experts to predict per‑pixel reliability scales, and a hybrid attention mechanism that gates unreliable cells and balances modalities. The approach also includes a Laplace uncertainty head to signal severe or combined sensor failures, and demonstrates improved closed‑loop robustness on the CARLA Longest6 benchmark across various degradation scenarios.