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arXiv 2506.05965cs.CV
Dy3DGS-SLAM：面向动态环境的单目3D高斯泼溅SLAM
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Engineering Sciences, Saarland University of Applied Sciences（应用科学大学工程科学学院，萨尔兰应用科学大学）；Fraunhofer Institute for Nondestructive Testing（非破坏性检测研究所，弗劳恩霍夫）；College of Information Science and Technology, Beijing University of Chemical Technology（信息科学与技术学院，北京化工大学）；Faculty of Science and Engineering, Laval University（科学与工程学院，拉瓦尔大学）
Mingrui Li, Yiming Zhou, Hongxing Zhou, Xinggang Hu, Florian Roemer, Hongyu Wang, Ahmad Osman
Current Simultaneous Localization and Mapping (SLAM) methods based on Neural Radiance Fields (NeRF) or 3D Gaussian Splatting excel in reconstructing static 3D scenes but struggle with tracking and reconstruction in dynamic environments, such as real-world scenes with moving elements. Existing NeRF-based SLAM approaches addressing dynamic challenges typically rely on RGB-D inputs, with few methods accommodating pure RGB input. To overcome these limitations, we propose Dy3DGS-SLAM, the first 3D Gaussian Splatting (3DGS) SLAM method for dynamic scenes using monocular RGB input. To address dynamic interference, we fuse optical flow masks and depth masks through a probabilistic model to obtain a fused dynamic mask. With only a single network iteration, this can constrain tracking scales and refine rendered geometry. Based on the fused dynamic mask, we designed a novel motion loss to constrain the pose estimation network for tracking. In mapping, we use the rendering loss of dynamic pixels, color, and depth to eliminate transient interference and occlusion caused by dynamic objects. Experimental results demonstrate that Dy3DGS-SLAM achieves state-of-the-art tracking and rendering in dynamic environments, outperforming or matching existing RGB-D methods.
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is that dense monocular SLAM provides the right information to fit a neural radiance field of the scene in real-time, by providing accurate pose estimates and depth-maps with associated uncertainty. With our proposed uncertainty-based depth loss, we achieve not only good photometric accuracy, but also great geometric ac…
RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting
Institute of Robotics and Automatic Information System, Nankai University（南开大学机器人与自动信息系统研究所）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；TBI center, Nankai University（南开大学TBI中心）
Kyushu University（九州大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Fukuoka University（福冈大学）；The University of Tokyo（东京大学）
Visual SLAM systems targeting static scenes have been developed with satisfactory accuracy and robustness. Dynamic 3D object tracking has then become a significant capability in visual SLAM with the requirement of understanding dynamic surroundings in various scenarios including autonomous driving, augmented and virtual reality. However, performing dynamic SLAM solely with monocular images remains a challenging problem due to the difficulty of associating dynamic features and estimating their positions. In this paper, we present MOTSLAM, a dynamic visual SLAM system with the monocular configur…
IIIT-Delhi（德里信息技术学院）；NEC Laboratories America, Inc.（NEC美国实验室公司）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely disjoint approaches towards building a 3D map of the surrounding environment. In this paper, we demonstrate that the coupling of these two by leveraging the strengths of each mitigates the other's shortcomings. Specifically, we propose a joint narrow and wide baseline based self-improving framework, where on the one hand the CNN-predicted depth is leveraged to perform pseudo RGB-D feature-based SLAM, leading to bett…
SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding
IRAIS, tjKLIR, College of Artificial Intelligence, Nankai University（IRAIS、tjKLIR、人工智能学院、南开大学）；IITRS, Shenzhen Research Institute, Nankai University（IITRS、深圳研究所、南开大学）
We present a Deep Learning based system for the twin tasks of localization and obstacle avoidance essential to any mobile robot. Our system learns from conventional geometric SLAM, and outputs, using a single camera, the topological pose of the camera in an environment, and the depth map of obstacles around it. We use a CNN to localize in a topological map, and a conditional VAE to output depth for a camera image, conditional on this topological location estimation. We demonstrate the effectiveness of our monocular localization and depth estimation system on simulated and real datasets.
GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting
Shanghai AI Laboratory（上海人工智能实验室）；Hong Kong University of Science and Technology（香港科技大学）；Fudan University（复旦大学）；Northwestern Polytechnical University（西北工业大学）；TeleAI, China Telecom Corp Ltd（中国电信集团有限公司TeleAI）
EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition
Texas A&M University（德克萨斯农工大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；University of Minnesota（明尼苏达大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
FeatureSLAM: Feature-enriched 3D gaussian splatting SLAM in real time
CVSSP, Computer Science and Electronic Engineering, University of Surrey（Surrey大学计算机科学与电子工程系）；Surrey Institute for People-Centered Artificial Intelligence, University of Surrey（Surrey大学人中心人工智能研究所）；Industrial 3D Robotics（Industrial 3D Robotics公司）
State Key Laboratory of Robotics and Systems (HIT), Harbin Institute of Technology（机器人系统国家重点实验室（哈工大））；Yangtze River Delta HlT Robot Technology Research Institute（长江三角洲HLT机器人技术研究院）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）
SCE-SLAM: Scale-Consistent Monocular SLAM via Scene Coordinate Embeddings
School of Computer Science and Engineering, State Key Laboratory of Complex & Critical Software Environment, Jiangxi Research Institute, Beihang University（计算机科学与工程学院、复杂与关键软件环境国家重点实验室、江西研究院、北航）；Macquarie University（麦考瑞大学）；Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology（北京半导体神经网络智能感知与计算技术重点实验室）
M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Shanghai Innovation Institute（上海创新研究院）；Zhejiang University（浙江大学）；Beijing Institute of Technology（北京理工大学）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）
Reutlingen University（罗伊特林根大学）；University of Stellenbosch（斯泰伦博斯大学）
SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to a…
DFKI - German Research Center for Artificial Intelligence（德国人工智能研究中心）
This paper demonstrates a system capable of combining a sparse, indirect, monocular visual SLAM, with both offline and real-time Multi-View Stereo (MVS) reconstruction algorithms. This combination overcomes many obstacles encountered by autonomous vehicles or robots employed in agricultural environments, such as overly repetitive patterns, need for very detailed reconstructions, and abrupt movements caused by uneven roads. Furthermore, the use of a monocular SLAM makes our system much easier to integrate with an existing device, as we do not rely on a LiDAR (which is expensive and power consum…
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
Columbia University（哥伦比亚大学）；Cloudspace Technology（云空间科技）；The University of Tokyo（东京大学）；Dianjing Ciyuan Culture（电境慈源文化）；Dalian University of Technology（大连理工大学）
National Taiwan University（国立台湾大学）；National Yang Ming Chiao Tung University（国立阳明交通大学）；Mobile Drive Technology（摩智科技）
A spatial AI that can perform complex tasks through visual signals and cooperate with humans is highly anticipated. To achieve this, we need a visual SLAM that easily adapts to new scenes without pre-training and generates dense maps for downstream tasks in real-time. None of the previous learning-based and non-learning-based visual SLAMs satisfy all needs due to the intrinsic limitations of their components. In this work, we develop a visual SLAM named Orbeez-SLAM, which successfully collaborates with implicit neural representation and visual odometry to achieve our goals. Moreover, Orbeez-SL…
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）
GARAD-SLAM: 3D GAussian splatting for Real-time Anti Dynamic SLAM
School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；School of Aeronautics and Astronautics, Sun Yat-sen University（中山大学航空航天学院）；Faculty of Science and Technology, University of Macau（澳门大学科技学院）
AgriGS-SLAM: Orchard Mapping Across Seasons via Multi-View Gaussian Splatting SLAM
Dipartimento di Bioingegneria, Elettronica e Informazione, Politecnico di Milano（生物工程、电子与信息系，米兰理工大学）；Agricultural Biosystems Engineering, Wageningen University & Research（农业生物系统工程，瓦赫宁根大学与研究）
