arXiv Computer Vision
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU
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arXiv Computer Vision
Oct 28
LingGS: Decoupling Feed-Forward Geometry Prediction from Camera Tracking for High-Fidelity Gaussian SLAM
The paper introduces LingGS, an online monocular RGB Gaussian splatting SLAM system that decouples feed‑forward geometry prediction from camera tracking. By leveraging a frozen streaming 3D foundation model as a virtual RGB‑D sensor for depth estimation and an explicit geometric solver for camera motion via feature reprojection, LingGS achieves higher tracking accuracy than learned predictions. Experiments on indoor and outdoor datasets demonstrate significant improvements in reconstruction quality, achieving about 7 FPS on a single RTX 4090 and 2–3× the throughput of prior SLAM systems.
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
Sep 18
AMB3R-SLAM: Kilometer-scale SLAM with Hierarchical Backend
AMB3R‑SLAM is a real‑time monocular SLAM system that can reconstruct kilometer‑scale trajectories over 10,000 frames on a single consumer‑grade GPU. It combines a lightweight front‑end for low‑latency tracking with a hierarchical backend that enforces local, mid‑level, and global consistency, avoiding bundle adjustment and thus handling dynamic scenes naturally. The system also supports stereo, RGB‑D, and LiDAR inputs, achieving strong camera tracking performance and reducing absolute trajectory error by over 70% on several datasets, with sub‑meter accuracy when LiDAR is added.
arXiv Machine Learning
Aug 27
Minimalist Visual Inertial Odometry
The paper introduces a minimalist visual-inertial odometry system that uses only four downward-facing photodiodes with optical Gabor masks and an IMU to estimate motion for differential-drive robots. By jointly optimizing mask parameters and a Temporal Convolutional Network in a physically-grounded simulator, the model decodes speed from the photodiode signals and combines it with IMU angular speed to produce a continuous planar trajectory. Experiments on a prototype robot across indoor and outdoor terrains show that the system closely follows reference trajectories without real-world fine-tuning.
Hugging Face Trending Papers
Jul 23
GLAM-SLAM: Real-time Gaussian Large-scale Mapping via Flow Densification and Spatial Decomposition
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.
Hugging Face Trending Papers
Jun 24
DSP-SLAM++: A Unified Framework for Multi-Class, High-Fidelity Object SLAM in the Wild
Existing object-aware SLAM systems force a trade-off between real-time performance, multi-class support, and the generation of high-fidelity, semantically coherent object models. To address this trade-off, we present DSP-SLAM++, which extends the DSP-SLAM framework with an asynchronous mapping pipeline for real-time performance and dedicated sensor fusion adaptations for a monocular fisheye-LiDAR suite.