For VSLAM (Visual Simultaneous Localization and Mapping), localization is a challenging task, especially for some challenging situations: textureless frames, motion blur, etc.. To build a robust exploration and localization system in a given space or environment, a submap-based VSLAM system is proposed in this paper. Our system uses a submap back-end and a visual front-end. The main advantage of our system is its robustness with respect to tracking failure, a common problem in current VSLAM algorithms. The robustness of our system is compared with the state-of-the-art in terms of average tracking percentage. The precision of our system is also evaluated in terms of ATE (absolute trajectory error) RMSE (root mean square error) comparing the state-of-the-art. The ability of our system in solving the `kidnapped' problem is demonstrated. Our system can improve the robustness of visual localization in challenging situations.
Grid-based Submap Joining: An Efficient Algorithm for Simultaneously Optimizing Global Occupancy Map and Local Submap Frames
Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
Efficient Submap-based Autonomous MAV Exploration using Visual-Inertial SLAM Configurable for LiDARs or Depth Cameras
Technical University of Munich（慕尼黑技术大学）；School of Computation, Information and Technology（计算、信息与技术学院）；Imperial College London（伦敦帝国学院）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）
Scalable Outdoors Autonomous Drone Flight with Visual-Inertial SLAM and Dense Submaps Built without LiDAR
Technical University of Munich（技术大学慕尼黑）；School of Computation, Information and Technology（计算、信息与技术学院）；Mobile Robotics Lab（移动机器人实验室）；Department of Computing（计算学院）；Imperial College London（伦敦帝国学院）；Department of Mechanical and Process Engineering（机械与过程工程学院）；ETH Zurich（苏黎世联邦理工学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
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）
GRS-SLAM3R: Real-Time Dense SLAM with Gated Recurrent State
Shanghai Jiao Tong University（上海交通大学）；Institute of Medical Robotics（医疗机器人研究所）；School of Automation and Intelligent Sensing（自动化与智能感知学院）；Key Laboratory of System Control and Information Processing（系统控制与信息处理重点实验室）；Ministry of Education of China（中华人民共和国教育部）
OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS
Mobile Robotics Lab, School of Computation, Information and Technology (CIT) at the Technical University of Munich (TUM)（慕尼黑技术大学移动机器人实验室，计算信息与技术学院）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所）；Mobile Robotics Lab, ETH Zurich（苏黎世联邦理工学院移动机器人实验室）
Meituan UAV（美团无人机）；School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Dept. of Mechanical Engineering, Computer and Information Sciences, University of Delaware（德雷塞尔大学机械工程、计算机与信息科学系）
GPGM-SLAM: a Robust SLAM System for Unstructured Planetary Environments with Gaussian Process Gradient Maps
Institute of Robotics and Mechatronics（机器人与机电一体化研究所）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；Centre for Autonomous Systems（自主系统中心）；University of Technology Sydney（悉尼科技大学）
同时定位与地图构建（SLAM）技术由于能够校正定位误差并随时间产生一致的环境地图而对移动机器人的长期自主性起着关键作用。与城市或人造环境相似，其中独特的物体和结构的存在为定位提供了独特的线索，非结构化自然环境的外观通常是模糊的和自相似的，阻碍了环路闭合检测的性能。在本文中，我们提出了一种方法，以提高鲁棒性的地方识别的背景下，基于子地图的立体SLAM高斯过程梯度地图（GPGMaps）。GPGMaps通过高斯过程回归和结构化核插值嵌入了局部地形高程梯度的连续表示，仅考虑噪声高程测量。我们利用GPGMaps的类似图像的结构，使用传统的视觉特征和Bag of Words来检测循环闭合。GPGMap匹配作为SE（2）对齐来执行，以在姿态图内建立循环闭合约束。我们评估了拟议的管道上记录的各种数据集山。埃特纳火山，西西里岛和摩洛哥沙漠，分别月球和火星般的环境中，我们比较的本地化性能与国家的最先进的方法，视觉SLAM和视觉环路闭合检测。
Enhancing exploration algorithms for navigation with visual SLAM
Artificial Intelligence Research Institute, Federal Research Center for 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人工智能研究所）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
Graph-based SLAM-Aware Exploration with Prior Topo-Metric Information
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局信息通信研究所）
提出一种基于先验拓扑度量图的 SLAM 感知探索规划方法，通过全局回环增强路径并以贪心算法剪枝，在保持探索效率的同时提升建图精度。
Contact SLAM: An Active Tactile Exploration Policy Based on Physical Reasoning Utilized in Robotic Fine Blind Manipulation Tasks
Research Center for Intelligent Robotics（智能机器人研究中心）；National Key Laboratory of Aerospace Flight Dynamics（航空飞行动力学国家重点实验室）；Shaanxi Province Innovation Team of Intelligent Robotic Technology（陕西省智能机器人技术创新团队）；School of Astronautics, Northwestern Polytechnical University（航空学院，西北工业大学）
Collaborative Graph Exploration with Reduced Pose-SLAM Uncertainty via Submodular Optimization
School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）；Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局信息通信研究所）
MA-SLAM: Active SLAM in Large-Scale Unknown Environment using Map Aware Deep Reinforcement Learning
Sun Yat-sen University（中山大学）；South China University of Technology（华南理工大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；City University of Hong Kong（香港城市大学）
Center for Artificial Intelligence \& Robotics (CAIR) New York University Abu Dhabi (NYUAD) United Arab Emirates；Robotics \& Intelligent Systems Control Lab NYUAD United Arab Emirates
Fast Autonomous Robotic Exploration Using the Underlying Graph Structure
Departamento de Informática e Ingeniería de Sistemas（信息与系统工程系）；Instituto de Investigación en Ingeniería de Aragón（阿拉贡工程研究所）；Universidad de Zaragoza（萨拉戈萨大学）
