In this paper, a simultaneous localization and mapping (SLAM) method that eliminates the influence of moving objects in dynamic environments is proposed. This method utilizes the correlation between map points to separate points that are part of the static scene and points that are part of different moving objects into different groups. A sparse graph is first created using Delaunay triangulation from all map points. In this graph, the vertices represent map points, and each edge represents the correlation between adjacent points. If the relative position between two points remains consistent over time, there is correlation between them, and they are considered to be moving together rigidly. If not, they are considered to have no correlation and to be in separate groups. After the edges between the uncorrelated points are removed during point-correlation optimization, the remaining graph separates the map points of the moving objects from the map points of the static scene. The largest group is assumed to be the group of reliable static map points. Finally, motion estimation is performed using only these points. The proposed method was implemented for RGB-D sensors, evaluated with a public RGB-D benchmark, and tested in several additional challenging environments. The experimental results demonstrate that robust and accurate performance can be achieved by the proposed SLAM method in both slightly and highly dynamic environments. Compared with other state-of-the-art methods, the proposed method can provide competitive accuracy with good real-time performance.
All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking
Center for Artificial Intelligence Research, Korea Institute of Science and Technology（韩国科学技术院人工智能研究中心）；Korea University（高丽大学）；KIST School (UST)（韩国科学技术院学校（科学技术联合大学院））
PG-SLAM: Photo-realistic and Geometry-aware RGB-D SLAM in Dynamic Environments
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Technical University of Munich（慕尼黑工业大学）；Shanghai Jiao Tong University（上海交通大学）
STAMICS: Splat, Track And Map with Integrated Consistency and Semantics for Dense RGB-D SLAM
University of Science and Technology of China（中国科学技术大学）；University of Science and Technology Liaoning（辽宁科技大学）；Beijing Institute of Technology（北京理工大学）；Beihang University（北京航空航天大学）
HI-SLAM2: Geometry-Aware Gaussian SLAM for Fast Monocular Scene Reconstruction
Institute for Photogrammetry and Geoinformatics, University of Stuttgart（摄影测量与地理信息学研究所，斯图加特大学）；Technical University of Munich（慕尼黑技术大学）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）
RoDyn-SLAM: Robust Dynamic Dense RGB-D SLAM with Neural Radiance Fields
School of Data Science, Fudan University（复旦大学数据科学学院）；Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；ByteDance（字节跳动）
RoDyn-SLAM 提出一种基于神经辐射场的动态稠密 RGB-D SLAM 框架，通过融合光流与语义的运动掩码滤除动态干扰，并采用分而治之位姿优化与边缘扭曲损失，在准确性和鲁棒性上达到最先进水平。
MCGS-SLAM: A Multi-Camera SLAM Framework Using Gaussian Splatting for High-Fidelity Mapping
Department of Mathematics, ETH Zurich（苏黎世联邦理工学院数学系）；Department of Mechanical and Process Engineering, ETH Zurich（苏黎世联邦理工学院机械与工艺工程系）；Department of Informatics, University of Zurich（苏黎世大学信息系）；Institute for Photogrammetry, University of Stuttgart（斯图加特大学摄影测量研究所）；Computer Vision and Geometry Group, ETH Zurich（苏黎世联邦理工学院计算机视觉与几何组）；Microsoft Spatial AI Lab（微软空间AI实验室）；Computer Vision Research Group, University of Amsterdam（阿姆斯特丹大学计算机视觉研究组）
EC-SLAM: Effectively Constrained Neural RGB-D SLAM with Sparse TSDF Encoding and Global Bundle Adjustment
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University（复旦大学类脑智能科学与技术研究院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）
Towards Real-time Semantic RGB-D SLAM in Dynamic Environments
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
MAGS-SLAM: Monocular Multi-Agent Gaussian Splatting SLAM for Geometrically and Photometrically Consistent Reconstruction
ETH Zurich（苏黎世联邦理工学院）；Harbin Engineering University（哈尔滨工程大学）；University of Liverpool（利物浦大学）；University of Macau（澳门大学）；University of Ottawa（多伦多大学）；Wuhan University（武汉大学）；Imperial College London（伦敦帝国学院）
RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic Objects
University of Edinburgh（爱丁堡大学）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
EGS-SLAM: RGB-D Gaussian Splatting SLAM with Events
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；School of Instrumentation and Optoelectronics Engineering, Beihang University, Beijing, China（北京航空航天大学仪器与光电工程学院）
本文提出 EGS-SLAM 框架，通过融合事件数据与 RGB-D 输入并建模相机连续曝光轨迹，有效解决运动模糊问题，实现了高精度的稳健跟踪与高保真 3D 高斯泼溅重建。
Black-box Adversarial Attacks on CNN-based SLAM Algorithms
Department of Electrical and Computer Engineering, University of Thessaly（电气与计算机工程系，塞萨洛尼基大学）；Department of Computer Science, William & Mary（计算机科学系，威廉玛丽学院）
MyGO-Splat: Multi-Objective Closed-Loop Geometric Feedback for RGB-Only Gaussian SLAM
HFIPS, Chinese Academy of Sciences（中国科学院合肥智能机械研究所）；University of Science and Technology of China（中国科学技术大学）；Tohoku University（东北大学）；University of Zaragoza（阿斯图里亚大学）；University of Tokyo（东京大学）；Dalian University of Technology（大连理工大学）
RGB-D-Inertial SLAM in Indoor Dynamic Environments with Long-term Large Occlusion
University of Edinburgh（爱丁堡大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；Touchlab Limited（触觉实验室有限公司）；the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
