arXiv:2609.38054v1 Announce Type: cross
Abstract: We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping. Inspired by MA...
By Christopher Kolios, Ishaan Mehta, Sasa Janjic, Yeganeh Bahoo, Sajad Saeedi
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.
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:2607.24495v2 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
arXiv:2405.07392v4 Announce Type: replace-cross
Abstract: Many existing visual SLAM methods can achieve high localization accuracy in dynamic environments by leveraging deep learning to mask moving o...
By Yuhao Zhang, Mihai Bujanca, Mikel Luj\'an
WorkIng is a dual-domain benchmark for autonomous navigation in highway work zones, offering a real-world dataset (WorkIng-Highway) with 360° multi‑modal sensor data and over 12,000 annotated frames, and a synthetic counterpart (WorkIng-CARLA) that simulates 24 regulation‑aware scenarios across six topologies and traffic standards. The benchmark includes 227,399 tracked 3D bounding boxes and 100+ custom 3D assets. Experiments show that real‑world data drives performance, while scenario‑stratified synthetic augmentation improves 2D recognition, 3D detection, and trajectory planning.
By Xujun Xie, Dominik R\"o{\ss}le, Adithya Mohan, Daniel Cremers, Torsten Sch\"on