In this paper, a robust RGB-D SLAM system is proposed to utilize the structural information in indoor scenes, allowing for accurate tracking and efficient dense mapping on a CPU. Prior works have used the Manhattan World (MW) assumption to estimate low-drift camera pose, in turn limiting the applications of such systems. This paper, in contrast, proposes a novel approach delivering robust tracking in MW and non-MW environments. We check orthogonal relations between planes to directly detect Manhattan Frames, modeling the scene as a Mixture of Manhattan Frames. For MW scenes, we decouple pose estimation and provide a novel drift-free rotation estimation based on Manhattan Frame observations. For translation estimation in MW scenes and full camera pose estimation in non-MW scenes, we make use of point, line and plane features for robust tracking in challenging scenes. %mapping Additionally, by exploiting plane features detected in each frame, we also propose an efficient surfel-based dense mapping strategy, which divides each image into planar and non-planar regions. Planar surfels are initialized directly from sparse planes in our map while non-planar surfels are built by extracting superpixels. We evaluate our method on public benchmarks for pose estimation, drift and reconstruction accuracy, achieving superior performance compared to other state-of-the-art methods. We will open-source our code in the future.
MG-SLAM: Structure Gaussian Splatting SLAM with Manhattan World Hypothesis
Department of Information Science and Technology and Department of Complexity Science and Engineering, The University of Tokyo（信息科学与技术系和复杂科学与工程系，东京大学）；Institute of Medical Robotics and Department of Automation, Shanghai Jiao Tong University（医疗机器人研究所和自动化系，上海交通大学）；Department of Mechanical Engineering, Columbia University（机械工程系，哥伦比亚大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）；Department of Computer Science, Dalian University of Technology（计算机科学系，大连理工大学）
Homography Decomposition Networks for Planar Object Tracking
Zhejiang University（浙江大学）；Alibaba-Zhejiang University Joint Institute of Frontier Technologies（阿里巴巴-浙江大学前沿技术联合研究院）；East China Normal University（华东师范大学）
Fast Projective Image Rectification for Planar Objects with Manhattan Structure
Federal Research Center “Computer Science and Control” of Russian Academy of Sciences（俄罗斯科学院“计算机科学与控制”联邦研究中心）；Moscow Institute of Physics and Technology (State University)（莫斯科物理技术学院（国立大学））；The Institute for Information Transmission Problems of Russian Academy of Sciences（俄罗斯科学院信息传输问题研究所）；Smart Engines Service LLC（Smart Engines服务有限责任公司）
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））
PlanarTrack: A high-quality and challenging benchmark for large-scale planar object tracking
organization= Institute of Software, Chinese Academy of Sciences , city= Beijing , country= China；organization= University of Chinese Academy of Sciences , city= Beijing , country= China；organization= Dept. of Computer Science \& Engineering, University of North Texas , city= Denton , country= United States of America
School of Information Engineering, Zhengzhou University（郑州大学信息工程学院）；HiScene Information Technologies（海视信息技术公司）；Computer & Information Sciences Department, Temple University（天普大学计算机与信息科学系）；School of Computer Science and Communication Engineering, Jiangsu University（江苏大学计算机科学与通信工程学院）
PathFinder: Attention-Driven Dynamic Non-Line-of-Sight Tracking with a Mobile Robot
School of Electrical, Computer and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；School for Engineering of Matter, Transport and Energy, Arizona State University（亚利桑那州立大学物质、运输与能源工程学院）
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The 38th Research Institute of China Electronic Technology Group Corporation（中国电子科技集团公司第三十八研究所）；China Telecom Cloud Company（中国电信云公司）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程研究中心）；Shanghai Engineering Research Center of Energy Efficient and Custom AI IC（上海高效节能与定制AI芯片工程研究中心）
Non-planar Object Detection and Identification by Features Matching and Triangulation Growth
POLITECNICO DI MILANO School of Industrial and Information Engineering（米兰理工大学工业与信息工程学院）；Master of Science Degree in Computer Science and Engineering Department of Electronics, Information and Bioengineering（电子、信息与生物工程学院）
