arXiv Computer Vision
Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Aug 31
Failure or Drift? Evaluating Monocular SLAM under Synthetic and Real-World Corruptions
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 Computer Vision
Sep 15
SCOUT-SLAM: Structurally-Coupled Dual Uncertainty-Aware 3DGS SLAM in the Wild
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...
arXiv Computer Vision
Sep 15
SLAM Adversarial Lab: An Extensible Framework for Visual SLAM Robustness Evaluation under Adverse Conditions
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 Computer Vision
Sep 15
ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments
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 Computer Vision
2d ago
VCS-SLAM: Visibility-Consistent Semantic Fusion for 3D Gaussian SLAM
arXiv:2606.29494v3 Announce Type: replace Abstract: Semantic 3D Gaussian SLAM commonly fuses 2D semantic priors into a persistent 3D map using uniform optimization weights. However, such priors are n...
arXiv Computer Vision
Oct 9
Does Dynamic-Point Filtering Help When Texture Is Scarce? A Controlled Study of ORB-SLAM2 Front-Ends in Synthetic Indoor Scenes
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."