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.
By Da Li, Yihan Zang, Ivan Viola, Zeyu Wang, Dominik Engel
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...
By Ting-Wei Ou, Huang-Ting Lin, Kuu-Young Young
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.
By Hengyi Wang, Lourdes Agapito
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.
By Francesco Pasti, Jeremy Klotz, Nicola Bellotto, Shree K. Nayar
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.
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.
arXiv:2608.29003v1 Announce Type: cross
Abstract: In dynamic and unstructured environments, conventional SLAM systems generally suffer from significant accuracy degeneration due to their static assum...
By Wenting Wang, Jiaxin Guo, Wenzhen Dong, Yun-Hui Liu, Charlie C. L. Wang, Yeung Yam
arXiv:2608.23290v1 Announce Type: new
Abstract: Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely o...
By Antoni Valls, Jordi Sanchez-Riera
arXiv:2607.06023v2 Announce Type: replace
Abstract: Visual SLAM is a well-established technology utilized in a wide range of real-world applications. However, its performance still degrades under cha...
By Giovanni Cioffi, Davide Scaramuzza
arXiv:2610.10181v1 Announce Type: new
Abstract: Robots operating in dynamic environments require reliable detection of how their surroundings change over time. Existing learning-based methods largely...
By Ruihan Xu, Jiae Yoon, Kaichen Zhou, Ue-Hwan Kim, Luca Carlone
arXiv:2607.24495v2 Announce Type: replace
Abstract: Structured-light (SL) cameras power depth sensing in millions of devices, and recent neural SL decoding methods have substantially improved their d...
By Jiaheng Li, Binsheng Zhang, Xinhai Chang, Wenzheng Chen
Robust dynamic object detection and tracking are essential for enabling robots to operate safely and effectively alongside humans in complex environments such as construction sites. While LiDAR-based SLAM and occupancy grid methods offer viable solutions for detecting and tracking motion, many state-of-the-art 3D vision approaches rely heavily on pre-trained neural networks and require additional post-processing to identify moving objects.