State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a problem. In this paper, we propose a tightly coupled multi-sensor fusion framework, Lvio-Fusion, which fuses stereo camera, Lidar, IMU, and GPS based on the graph optimization. Especially for urban traffic scenes, we introduce a segmented global pose graph optimization with GPS and loop-closure, which can eliminate accumulated drifts. Additionally, we creatively use a actor-critic method in reinforcement learning to adaptively adjust sensors' weight. After training, actor-critic agent can provide the system better and dynamic sensors' weight. We evaluate the performance of our system on public datasets and compare it with other state-of-the-art methods, which shows that the proposed method achieves high estimation accuracy and robustness to various environments. And our implementations are open source and highly scalable.
School of Integrated Circuits, Tsinghua University（清华大学集成电路学院）；Tsinghua University（清华大学）
Simultaneous localization and mapping (SLAM) is a critical technology that enables autonomous robots to be aware of their surrounding environment. With the development of deep learning, SLAM systems can achieve a higher level of perception of the environment, including the semantic and text levels. However, current works are limited in their ability to achieve a natural-language level of perception of the world. To address this limitation, we propose LP-SLAM, the first language-perceptive SLAM system that leverages large language models (LLMs). LP-SLAM has two major features: (a) it can detect…
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）
POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments
The University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所）；The Technical University of Munich（慕尼黑工业大学）；Clearpath Robotics（Clearpath机器人公司）；The Technical University of Nuremberg（纽伦堡工业大学）
Reliability of SLAM systems is considered one of the critical requirements in modern autonomous systems. This directed the efforts to developing many state-of-the-art systems, creating challenging datasets, and introducing rigorous metrics to measure SLAM performance. However, the link between datasets and performance in the robustness/resilience context has rarely been explored. In order to fill this void, characterization of the operating conditions of SLAM systems is essential in order to provide an environment for quantitative measurement of robustness and resilience. In this paper, we arg…
Massachusetts Institute of Technology (MIT)（麻省理工学院（MIT））；Northeastern University（东北大学）
Simultaneous localization and mapping (SLAM) is a critical capability in autonomous navigation, but in order to scale SLAM to the setting of "lifelong" SLAM, particularly under memory or computation constraints, a robot must be able to determine what information should be retained and what can safely be forgotten. In graph-based SLAM, the number of edges (measurements) in a pose graph determines both the memory requirements of storing a robot's observations and the computational expense of algorithms deployed for performing state estimation using those observations; both of which can grow unbo…
DATA61, CSIRO（联邦科学与工业研究组织DATA61）；Queensland University of Technology（昆士兰科技大学）
The concept of continuous-time trajectory representation has brought increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM. However, regardless of these advantages, its offline property caused by the requirement of global batch optimization is critically hindering its relevance for real-time and life-long applications. In this paper, we present a dense map-centric SLAM method based on a continuous-time trajectory to cope with this problem. The proposed system locally functions in a similar fashion to conventional Continuous-Time SLAM (CT-SLAM). However, it removes the n…
University of California, Los Angeles（加州大学洛杉矶分校）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Sensing and Robotics for Infrastructure Laboratory（基础设施感知与机器人实验室）；Computer Science and AI Lab (CSAIL) at the Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；MIT-WHOI Joint Program in Oceanography/Applied Ocean Science & Engineering（麻省理工学院-WHOI联合海洋学/应用海洋科学与工程计划）；Robust Autonomy Lab（自主性实验室）
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University（湖南大学汽车车身先进设计制造国家重点实验室）
This paper proposes a new method that fuses acoustic measurements in the reverberation field and low-accuracy inertial measurement unit (IMU) motion reports for simultaneous localization and mapping (SLAM). Different from existing studies that only use acoustic data for direction-of-arrival (DoA) estimates, the source's distance from sensors is calculated with the direct-to-reverberant energy ratio (DRR) and applied as a new constraint to eliminate the nonlinear noise from motion reports. A particle filter is applied to estimate the critical distance, which is key for associating the source's …
Ultra-Fusion: A Resilient Tightly-Coupled Multi-Sensor Fusion SLAM Framework under Sensor Degradation and Spatiotemporal Perturbation for Intelligent Transportation Systems
Beijing Institute of Technology（北京理工大学）；Chongqing University（重庆大学）；Sichuan University（四川大学）；Northwestern Polytechnical University（西北工业大学）；Shanghai Jiao Tong University（上海交通大学）
ETH Zürich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；Universidad Sergio Arboleda（塞尔吉奥·阿博莱达大学）；Pontificia Universidad Javeriana（哈维里亚那天主教大学）；University of Adelaide（阿德莱德大学）；Australian Center for Robotic Vision（澳大利亚机器人视觉中心）
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term …
Doppler-SLAM: Doppler-Aided Radar-Inertial and LiDAR-Inertial Simultaneous Localization and Mapping
Julius-Maximilians-Universität Würzburg（乌尔姆-马克斯-普朗克大学）；Zentrum für Telematik e.V.（电信研究中心）；Nuremberg Institute of Technology Georg Simon Ohm（纽伦堡技术学院 Georg Simon Ohm）；CARIAD SE（CARIAD SE公司）；University of Bonn（波恩大学）；Center for Robotics（机器人中心）；Lamarr Institute for Machine Learning and Artificial Intelligence（Lamarr机器学习与人工智能研究所）
Stevens Institute of Technology（斯蒂文斯理工学院）；Exyn Technologies（埃克森科技公司）
Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. W…
Autonomous navigation requires an accurate model or map of the environment. While dramatic progress in the prior two decades has enabled large-scale SLAM, the majority of existing methods rely on non-linear optimization techniques to find the MLE of the robot trajectory and surrounding environment. These methods are prone to local minima and are thus sensitive to initialization. Several recent papers have developed optimization algorithms for the Pose-Graph SLAM problem that can certify the optimality of a computed solution. Though this does not guarantee a priori that this approach generates …
Simultaneous Localization and Mapping, commonly known as SLAM, has been an active research area in the field of Robotics over the past three decades. For solving the SLAM problem, every robot is equipped with either a single sensor or a combination of similar/different sensors. This paper attempts to review, discuss, evaluate and compare these sensors. Keeping an eye on future, this paper also assesses the characteristics of these sensors against factors critical to the long-term autonomy challenge.
University of Science and Technology Beijing（北京科技大学）；Nazarbayev University（纳扎尔巴耶夫大学）；University of Jordan（约旦大学）
Multi-robot visual simultaneous localization and mapping (SLAM) system is normally consisted of multiple mobile robots equipped with camera and/or other visual sensors. The networked robots work independently or cooperatively in an unknown scene in order to solve autonomous localization and mapping problem. One of the most critical issues in Multi-robot visual SLAM is the intensive computation that is normally required yet overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy and dense point cloud map construction …
3D Foundation Model-Based Loop Closing for Decentralized Collaborative SLAM
Department of Computer and Software Engineering, Polytechnique Montréal（计算机与软件工程系，蒙特利尔理工学院）；Oxford Robotics Institute, Dept. of Engineering Science, University of Oxford（牛津大学机器人研究所，工程科学系）
Leveraging Semantic Graphs for Efficient and Robust LiDAR SLAM
College of Intelligence Science and Technology and the National Key Laboratory of Equipment State Sensing and Smart Support, National University of Defense Technology, China（智能科学与技术学院和装备状态感知与智能支撑国家重点实验室，国防科技大学，中国）；T Stone Robotics Institute and Department of Mechanical and Automation Engineering, the Chinese University of Hong Kong, China（石墨机器人研究院和机械与自动化工程系，香港中文大学，中国）
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（麻省理工学院信息与决策系统实验室）
In this work we present the first initialization methods equipped with explicit performance guarantees adapted to the pose-graph simultaneous localization and mapping (SLAM) and rotation averaging (RA) problems. SLAM and rotation averaging are typically formalized as large-scale nonconvex point estimation problems, with many bad local minima that can entrap the smooth optimization methods typically applied to solve them; the performance of standard SLAM and RA algorithms thus crucially depends upon the quality of the estimates used to initialize this local search. While many initialization met…
Multi-Sensor Terrestrial SLAM for Real-Time, Large-Scale, and GNSS-Interrupted Forest Mapping
Faculty of Science and Technology, Norwegian University of Life Sciences (NMBU)（挪威生命科学大学科学与技术学院）；Norwegian Institute of Bioeconomy Research (NIBIO)（挪威生物经济研究所）
SMapper: A Multi-Modal Data Acquisition Platform for SLAM Benchmarking
Automation and Robotics Research Group (ARG), Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg（卢森堡大学自动化与机器人研究组（ARG）、安全、可靠与信任跨学科研究中心（SnT））
Spatiotemporal Calibration and Ground Truth Estimation for High-Precision SLAM Benchmarking in Extended Reality
Advanced Display and Sensing Research Center, Yongjiang Laboratory, Ningbo, China（先进显示与感知研究中心，永江实验室，宁波，中国）；Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, China（上海技术物理研究所，中国科学院，上海，中国）
University of Toronto（多伦多大学）；University of Waterloo（滑铁卢大学）；Uber Advanced Technologies Group（优步先进技术集团）
Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magn…
Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions
Beijing Institute of Technology（北京理工大学）；University of Science and Technology Beijing（北京科技大学）；Nanyang Technological University（南洋理工大学）；Shanghai AI Laboratory（上海人工智能实验室）
MonoSLAM: Robust Monocular SLAM with Global Structure Optimization
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；School of International Education, Wuhan University of Technology（武汉理工大学国际教育学院）；School of Mechanical Science and Engineering, Huazhong University of Science and Technology（华中科技大学机械科学与工程学院）
American University of Beirut（贝鲁特美国大学）；University of Waterloo（滑铁卢大学）
Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that cov…
Provably Guaranteed Polytopic Uncertainty Quantification for SLAM
School of Data Science, The Chinese University of Hong Kong, Shenzhen（数据科学学院，香港中文大学（深圳））；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（人工智能学院，香港中文大学（深圳））；Department of Electrical and Electronic Engineering, Imperial College London（电子与电气工程系，帝国理工学院伦敦分校）；School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney（航空航天、机械与机电工程学院，悉尼大学）
A Survey on Collaborative SLAM with 3D Gaussian Splatting
Institute of System Integration, Le Quy Don Technical University, Hanoi, 10000, Vietnam（越南吕文技术大学系统整合研究所）；Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology（日本先进科学技术研究院先进科学技术研究生院）；University of Engineering and Technology, Vietnam National University, Hanoi, 10000, Vietnam（越南国家大学工程技术大学）
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）
Tightly Coupled SLAM with Imprecise Architectural Plans
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学自动化与机器人研究组，安全、可靠与信任跨学科中心）；Faculty of Science, Technology and Medicine, University of Luxembourg（卢森堡大学科学、技术与医学学院）；I3A, Universidad de Zaragoza（阿拉维萨大学I3A）
University of Alberta（阿尔伯塔大学）；University of Toronto（多伦多大学）
Robustness and resilience of simultaneous localization and mapping (SLAM) are critical requirements for modern autonomous robotic systems. One of the essential steps to achieve robustness and resilience is the ability of SLAM to have an integrity measure for its localization estimates, and thus, have internal fault tolerance mechanisms to deal with performance degradation. In this work, we introduce a novel method for predicting SLAM localization error based on the characterization of raw sensor inputs. The proposed method relies on using a random forest regression model trained on 1-D global …
Passage-Aware Structural Mapping for RGB-D Visual SLAM
Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科研究中心）；University of Luxembourg（卢森堡大学）；Faculty of Science, Technology, and Medicine at the University of Luxembourg（卢森堡大学科学、技术与医学学院）；Universidad Politécnica de Madrid（马德里理工大学）
