Dynamic SLAM

SLAM: Simultaneous Localization and Mapping

Latest papers

12 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage Optimization
Oct 16, 2026Haorui Du, Lipu ZhouDynamic SLAM Visual SLAM
The authors are with the School of Instrument Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China
All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking
Oct 12, 2026Yewon Kim, Paul Hongsuck Seo, Sang Chul AhnDynamic SLAM Motion Segmentation
Center for Artificial Intelligence Research, Korea Institute of Science and Technology, Seoul, Korea (South) · Dept. of Computer Science and Engineering, Korea University, Seoul, Korea (South) · Center for Artificial Intelligence Research, Korea Institute of Science and Technology, and with AI-Robotics, KIST School (UST), Seoul, Korea (South)
Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM
Sep 23, 2026Preeti Chatterjee, Jin Lu, Jin Sun +1Dynamic SLAM Visual SLAM
School of Computing, University of Georgia Athens, GA 30602, USA
RoSe-SLAM: Robust Semantic-Aware Gaussian Splatting SLAM from Dynamic Monocular Videos
Aug 29, 2026Wenting Wang, Jiaxin Guo, Wenzhen Dong +3Dynamic SLAM Visual SLAM
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong SAR. · Department of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, UK. · Centre for Perceptual and Interactive Intelligence (CPII) Limited, Hong Kong SAR.
MoPe: Motion Permanence for Robust Monocular Gaussian Mapping in Dynamic Environments
Jun 28, 2026Qixin Xiao3D Gaussian Splatting Dynamic SLAM
University of Michigan
PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM
May 19, 2026Eunsoo Im, Gyeonggwan Lee, Seunghwan Hong +1Simultaneous Localization and Mapping Dynamic SLAM
KakaoMobility, South Korea
DynoSLAM: Dynamic SLAM with Generative Graph Neural Networks for Real-World Social Navigation
May 4, 2026Danil Tokhchukov, Veronika Morozova, Gonzalo FerrerSimultaneous Localization and Mapping Dynamic SLAM
Applied AI Institute, Moscow, Russia.
GGD-SLAM: Monocular 3DGS SLAM Powered by Generalizable Motion Model for Dynamic Environments
Apr 14, 2026Yi Liu, Haoxuan Xu, Hongbo Duan +5Dynamic SLAM 3DGS SLAM
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China. · Thrust of Robotics and Autonomous Systems, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China. · the School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, China. +1
DROID-SLAM in the Wild
Mar 19, 2026Moyang Li, Zihan Zhu, Marc Pollefeys +1Dynamic SLAM Visual SLAM
ETH Zurich · Microsoft
DAGS-SLAM: Dynamic-Aware 3DGS SLAM via Spatiotemporal Motion Probability and Uncertainty-Aware Scheduling
Feb 25, 2026Li Zhang, Yu-An Liu, Xijia Jiang +3Dynamic SLAM 3DGS SLAM
School of Mathematics, Hefei University of Technology, Hefei 230601, China · School of Software, Tsinghua University, Beijing 100084, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China
LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments
Feb 24, 2026Zeyu Jiang, Kuan Xu, Changhao ChenDynamic SLAM Visual SLAM
PEAK-Lab, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China · School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798
NGD-SLAM: Towards Real-Time Dynamic SLAM without GPU
May 12, 2024Yuhao Zhang, Mihai Bujanca, Mikel LujánSimultaneous Localization and Mapping Dynamic SLAM
University of Manchester. 2University of Cambridge. · 3Qualcomm Technologies XR Labs, Austria.

Oct 16, 2026J/K move · Enter open · S save

FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage Optimization

Haorui Du, Lipu Zhou

The authors are with the School of Instrument Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China

Dynamic SLAM Visual SLAM Dynamic Scene Reconstruction Motion Segmentation Optical Flow

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Monocular simultaneous localization and mapping (SLAM) remains challenging in dynamic environments. Real-time dynamic object identification in such environments is a critical problem. Recent approaches estimate per-pixel uncertainty from rendering residuals or multiview feature inconsistency to identify dynamic regions. These methods lack direct motion evidence, and their accuracy may degrade when reconstruction is inaccurate or features are ambiguous. In addition, such uncertainty merely downweights pixels in bundle adjustment (BA), so inaccurate uncertainty estimates can still impair camera pose estimation and 3D reconstruction. Accordingly, we present a monocular RGB SLAM system for dynamic scenes. Specifically, a motion uncertainty estimation method is proposed to derive uncertainty from the residual between optical flow and camera motion induced flow, directly reflecting object motion. The estimation varies with frame alignment accuracy, reducing false detections and yielding accurate pixel-level masks of dynamic regions. To make BA more robust, we estimate camera poses from high confidence pixels. These poses are then used to reproject dynamic pixels during joint refinement of poses and depth, reducing the influence of object motion on both pose and depth updates. Moreover, we provide a quantitative evaluation of dynamic masks and analyze how these masks affect the downstream tracking and reconstruction of dynamic objects. Our approach improves the mean dynamic mask IoU by 35% over a state-of-the-art baseline on DAVIS, and reduces the average ATE by 59% on Bonn and 19% on TUM relative to the SLAM backbone used in our system. The source code will be made publicly available.