In this letter, we propose MAROAM, a millimeter wave radar-based SLAM framework, which employs a two-step feature selection process to build the global consistent map. Specifically, we first extract feature points from raw data based on their local geometric properties to filter out those points that violate the principle of millimeter-wave radar imaging. Then, we further employ another round of probabilistic feature selection by examining how often and how recent the feature point has been detected in the proceeding frames. With such a two-step feature selection, we establish a global consistent map for accurate and robust pose estimation as well as other downstream tasks. At last, we perform loop closure and graph optimization in the back-end, further reducing the accumulated drift error.
We evaluate the performance of MAROAM on the three datasets: the Oxford Radar RobotCar Dataset, the MulRan Dataset and the Boreas Dataset. We consider a variety of experimental settings with different scenery, weather, and road conditions. The experimental results show that the accuracy of MAROAM is 7.95%, 37.0% and 8.9% higher than the currently best-performing algorithms on these three datasets, respectively. The ablation results also show that our map-based odometry performs 28.6% better than the commonly used scan-to-frames method. Finally, as devoted contributors to the open-source community, we will open source the algorithm after the paper is accepted.
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade their imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensing geometry and noise characteristics bring open challenges when being exploited for SLAM. % However, there are still open challenges since most existing visual and LiDAR …
Edinburgh Centre for Robotics（爱丁堡机器人中心）；Heriot-Watt University（赫瑞-瓦特大学）
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a pu…
Cheng Kar-Shun Robotics Institute, Hong Kong University of Science and Technology（香港科技大学郑家纯机器人研究所）；Hong Kong Center for Construction Robotics（香港建筑机器人中心）
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
State Key Laboratory of Ocean Sensing, Zhejiang University（浙江大学海洋传感国家重点实验室）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（浙江大学海洋交叉学科学生培养平台）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of AI and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）
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）
Dalian Maritime University（大连海事大学）；University of Birmingham（伯明翰大学）；RMIT University（皇家墨尔本理工大学）
Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the extent of landmarks is vital for precise navigation and path planning. Although object extent estimation has been successfully applied in target tracking, its adaption to SLAM remains unaddressed due to the additional uncertainty of the sensor platform, bias in the odometer reading, as well as the measurement non-linearity. In this paper, we propose to incorporat…
Dalian Maritime University（大连海事大学）；University of Birmingham（伯明翰大学）；RMIT University（皇家墨尔本理工大学）
This paper focuses on efficient landmark management in radar based simultaneous localization and mapping (SLAM). Landmark management is necessary in order to maintain a consistent map of the estimated landmarks relative to the estimate of the platform's pose. This task is particularly important when faced with multiple detections from the same landmark and/or dynamic environments where the location of a landmark can change. A further challenge with radar data is the presence of false detections. Accordingly, we propose a simple yet efficient rule based solution for radar SLAM landmark manageme…
KU Leuven（鲁汶大学）；imec（比利时微电子研究中心）；Gent University（根特大学）；VUB（布鲁塞尔自由大学）
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the brain. In contrast to most learning-based SLAM systems%, which a) require the acquisition of a representative dataset of the environment in which navigation must be performed and b) require an off-line training phase, our method does not require any offline training phase, but ra…
Rad-GS: Radar-Vision Integration for 3D Gaussian Splatting SLAM in Outdoor Environments
Department of Automation, School of Mechanical Engineering and Automation, Harbin Institute of Technology（自动化系、机械工程与自动化学院、哈尔滨工业大学）；School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院、哈尔滨工业大学）
SNAIL Radar: A large-scale diverse benchmark for evaluating 4D-radar-based SLAM
Wuhan University（武汉大学）；The state key Lab of Information Engineering in Surveying, Mapping And Remote Sensing (LIESMARS), Wuhan University（武汉大学测绘遥感信息工程国家重点实验室）；Wuhan Quantum Institute（武汉量子研究院）
DIDLM: A SLAM Dataset for Difficult Scenarios Featuring Infrared, Depth Cameras, LIDAR, 4D Radar, and Others under Adverse Weather, Low Light Conditions, and Rough Roads
School of Information Science and Technology, Northwest University（信息科学与技术学院，西北大学）；Department of Electrical and Computer Engineering, The University of British Columbia（电气与计算机工程系，不列颠哥伦比亚大学）
Breaking Time: A Fully Gaussian Framework for Distributed and Continuous-Time SLAM
Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome（计算机、控制与管理工程系（Antonio Ruberti）, 罗马萨皮恩扎大学）；University of Stuttgart（斯图加特大学）
University of Bologna（博洛尼亚大学）；CEA LETI（法国原子能和替代能源委员会电子信息技术实验室）
Digital maps will revolutionize our experience of perceiving and navigating indoor environments. While today we rely only on the representation of the outdoors, the mapping of indoors is mainly a part of the traditional SLAM problem where robots discover the surrounding and perform self-localization. Nonetheless, robot deployment prevents from a large diffusion and fast mapping of indoors and, further, they are usually equipped with laser and vision technology that fail in scarce visibility conditions. To this end, a possible solution is to turn future personal devices into personal radars as …
Registering the 4D Millimeter Wave Radar Point Clouds Via Generalized Method of Moments
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University（上海交通大学自动化与智能感知学院）；Institute of Medical Robotics, Shanghai Jiao Tong University（上海交通大学医疗机器人研究院）；Department of Statistics, Uppsala University（乌普萨拉大学统计系）
FIRE-LIVWO: Robust LiDAR-Inertial-Visual-Wheel Odometry via Failure-Immune mmWave Radar Enhancement
School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；Jiangsu Collaborative Innovation Center of Intelligent Mining Equipment, China University of Mining and Technology（中国矿业大学江苏省智能采矿装备协同创新中心）；National Key Laboratory of Intelligent Mining Equipment Technology, China University of Mining and Technology（中国矿业大学智能采矿装备技术全国重点实验室）
AgriLiRa4D: A Multi-Sensor UAV Dataset for Robust SLAM in Challenging Agricultural Fields
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；TopXGun Robotics（TopXGun机器人公司）；School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）
IIIT-Hyderabad（印度海得拉巴国际信息技术学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；University of Tartu（塔尔图大学）
Modern autonomous vehicles (AVs) often rely on vision, LIDAR, and even radar-based simultaneous localization and mapping (SLAM) frameworks for precise localization and navigation. However, modern SLAM frameworks often lead to unacceptably high levels of drift (i.e., localization error) when AVs observe few visually distinct features or encounter occlusions due to dynamic obstacles. This paper argues that minimizing drift must be a key desiderata in AV motion planning, which requires an AV to take active control decisions to move towards feature-rich regions while also minimizing conventional c…
LiDAR-BIND-T: Improved and Temporally Consistent Sensor Modality Translation and Fusion for Robotic Applications
IDLab - Faculty of Applied Engineering, University of Antwerp - imec（IDLab - 应用工程学院，安特卫普大学 - imec）；Cosys-Lab - Faculty of Applied Engineering, University of Antwerp（Cosys-Lab - 应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
本文提出 LiDAR-BIND-T，通过时间嵌入相似度、运动对齐损失和窗口化时间融合增强异构传感器到 LiDAR 的转换，并以新指标验证其提升 SLAM 稳定性与精度。
MOANA: Multi-Radar Dataset for Maritime Odometry and Autonomous Navigation Application
Institute of Advanced Machines and Design, SNU, Seoul, S. Korea（先进机械与设计研究所，首尔国立大学）；Dept. of Mechanical Engineering, SNU, Seoul, S. Korea（机械工程系，首尔国立大学）；Seadronix, Seoul, S. Korea（Seadronix公司）；Defence Science and Technology Agency, Singapore（新加坡国防科学与技术局）
Ghent University - imec（根特大学-imec）；University of Antwerp - Flanders Make（安特卫普大学-Flanders Make）
Biologically inspired algorithms for simultaneous localization and mapping (SLAM) such as RatSLAM have been shown to yield effective and robust robot navigation in both indoor and outdoor environments. One drawback however is the sensitivity to perceptual aliasing due to the template matching of low-dimensional sensory templates. In this paper, we propose an unsupervised representation learning method that yields low-dimensional latent state descriptors that can be used for RatSLAM. Our method is sensor agnostic and can be applied to any sensor modality, as we illustrate for camera images, rad…
Mobile robotic applications need precise information about the geometric position of the individual sensors on the platform. This information is given by the extrinsic calibration parameters which define how the sensor is rotated and translated with respect to a fixed reference coordinate system. Erroneous calibration parameters have a negative impact on typical robotic estimation tasks, e.g. SLAM. In this work we propose a new method for a continuous estimation of the calibration parameters during operation of the robot. The parameter estimation is based on the matching of point clouds which …
Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA（迈克·沃克66机械工程系，德克萨斯A&M大学，学院站，德克萨斯州，美国）；DEVCOM Army Research Laboratory, Adelphi, MD, USA（陆军研究实验室，阿德菲，马里兰州，美国）；University of Texas at Austin Center for Autonomy, Austin, TX, USA（德克萨斯大学奥斯汀分校自主性中心，奥斯汀，德克萨斯州，美国）
School of Intelligence Science and Technology（智能科学与技术学院）
The ability for a moving agent to localize itself in environment is the basic demand for emerging applications, such as autonomous driving, etc. Many existing methods based on multiple sensors still suffer from drift. We propose a scheme that fuses map prior and vanishing points from images, which can establish an energy term that is only constrained on rotation, called the direction projection error. Then we embed these direction priors into a visual-LiDAR SLAM system that integrates camera and LiDAR measurements in a tightly-coupled way at backend. Specifically, our method generates visual r…
Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and time-consuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in autonomous monitoring, transportation, and cleaning. However, unlike sailing in open water, the challenges posed by scenes of inland waterways, such as the complex distribution of obstacles, the global positioning system (GPS) signal denial environment, the reflection of bank-side structures, and the fog over the water surface, all impede USV applicati…
MAN Truck & Bus SE（曼恩卡车与客车公司）；Volkswagen AG（大众汽车公司）
Highly automated driving functions currently often rely on a-priori knowledge from maps for planning and prediction in complex scenarios like cities. This makes map-relative localization an essential skill. In this paper, we address the problem of localization with automotive-grade radars, using a real-time graph-based SLAM approach. The system uses landmarks and odometry information as an abstraction layer. This way, besides radars, all kind of different sensor modalities including cameras and lidars can contribute. A single, semantic landmark map is used and maintained for all sensors. We im…
