In this paper, we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consuming as low energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information. Moreover, we introduce two utility formulations to account for the presence of obstacles in the field of view and the robot's location. Through rigorous simulations, real robot experiments, and comparisons with state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy. This is obtained while keeping the traversed distance up to 39% shorter than the other methods and without increasing the wheels' total rotation amount. Code and implementation details are provided as open-source, and all the generated data is available on-line for consultation.
A review on cloud robotics based frameworks to solve simultaneous localization and mapping (slam) problem
National Institute of Technology, Raipur（印度国立理工学院赖布尔分校）；Motilal Nehru National Institute of Technology, Allahabad（莫蒂拉尔·尼赫鲁国立理工学院阿拉哈巴德分校）；Indian Institute of Information Technology, Allahabad（印度信息技术学院阿拉哈巴德分校）
Collaborative Radio SLAM for Multiple Robots based on WiFi Fingerprint Similarity
Singapore University of Technology and Design（新加坡科技设计大学）；Southwest University of Science and Technology（西南科技大学）；Nanyang Technological University（南洋理工大学）
SKiD-SLAM: Robust, Lightweight, and Distributed Multi-Robot LiDAR SLAM in Resource-Constrained Field Environments
Dept. Electr. and Comput. Eng.（电子与计算机工程系）；Inha University（inha大学）；Laboratory for Information & Decision Systems (LIDS)（信息与决策系统实验室）；Massachusetts Institute of Technology（麻省理工学院）
Task-driven SLAM Benchmarking For Robot Navigation
School of Electrical and Computer Engineering, and Institute of Robotics and Intelligent Machines, Georgia Institute of Technology（电气与计算机工程学院，机器人与智能机器研究所，佐治亚理工学院）；School of Mechanical Engineering and the School of Electrical and Computer Engineering, Georgia Institute of Technology（机械工程学院，电气与计算机工程学院，佐治亚理工学院）
人形机器人在现实生活中扮演着越来越重要的角色，特别是在室内应用方面。为室内环境测绘、自定位和物体识别等任务提供强大的解决方案，对于使机器人更加自主，从而更加人性化至关重要。著名的毕宿五服务机器人Pepper是实现这些目标的合适人选。在本文中，同时定位和映射（SLAM）算法与对象识别相结合的混合系统的开发和测试与胡椒机器人在现实世界的条件下第一次。ORB SLAM 2算法是本课题的一项重要研究工作.然后，基于尺度不变特征变换（SIFT）和随机采样一致性（RANSAC）的对象识别技术与SLAM相结合，识别和定位映射的室内环境中的对象。我们的实验结果表明，该系统的适用性，在现实世界的场景中的胡椒机器人。此外，我们在https://github.com/PaolaArdon/Salt-Pepper上向社区提供了我们的源代码。
NanoSLAM: Enabling Fully Onboard SLAM for Tiny Robots
Integrated Systems Laboratory of ETH Zürich（苏黎世联邦理工学院集成系统实验室）；Center for Project-Based Learning of ETH Zürich（苏黎世联邦理工学院项目导向学习中心）；Department of Electrical, Electronic and Information Engineering of University of Bologna（博洛尼亚大学电子、电子与信息工程系）
SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation
Microsoft（微软公司）；GRASP Laboratory, University of Pennsylvania（宾夕法尼亚大学GRASP实验室）；University of Pennsylvania（宾夕法尼亚大学）；Department of Electrical and Computer Engineering, University of California San Diego（加州大学圣地亚哥分校电气与计算机工程系）
GeVI-SLAM: Gravity-Enhanced Stereo Visua Inertial SLAM for Underwater Robots
School of Data Science, Chinese University of Hong Kong, Shenzhen, China（数据科学学院，香港中文大学（深圳））；School of Life Sciences, Chinese University of Hong Kong, China（生命科学学院，香港中文大学）
同时定位与地图构建（Simultaneous Localization And Mapping，SLAM）是一种基于光探测与测距（Light Detection And Ranging，LiDAR）和摄像机等传感器的观测来估计机器人位置并重建环境的任务。它广泛应用于自动驾驶和无人机交付等机器人应用。传统的基于LiDAR的SLAM算法主要利用场景的几何特征，而忽略了LiDAR的亮度信息。最近的一些基于深度学习的SLAM算法考虑强度特征并以端到端的方式训练姿态估计网络。然而，它们需要大量的数据汇集工作，并且它们对除受过训练的环境之外的环境的普遍性仍然不清楚。在本文中，我们引入强度功能的SLAM系统。我们提出了一个新的完整的SLAM框架，利用几何和强度功能。所提出的SLAM涉及基于强度的前端里程估计和基于强度的后端优化。进行了全面的实验，包括室外自动驾驶和室内仓库机器人操作。实验结果表明，该方法优于现有的仅几何LiDAR SLAM方法。
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）
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）
Decentralized Pose Graph Riemannian Optimization for Object-based Multi-Robot SLAM
College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海智能自主系统研究院）；Division of Decision and Control Systems, School of EECS, KTH Royal Institute of Technology（瑞典皇家理工学院电气工程与计算机科学学院决策与控制系统系）
TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments
Southern University of Science and Technology (SUSTech)（南方科技大学）；Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems（深圳市仿生机器人与智能系统重点实验室）；Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities（广东省高校人机增强与康复机器人重点实验室）
