RELEAD: Resilient Localization with Enhanced LiDAR Odometry in Adverse Environments
School of Systems Science and Engineering, Sun Yat-sen University（中山大学系统科学与工程学院）；Hong Kong University of Science and Technology（香港科技大学）；Hong Kong Polytechnic University（香港理工大学）
Zhiqiang Chen, Hongbo Chen, Yuhua Qi, Shipeng Zhong, Dapeng Feng, Wu Jin, Weisong Wen, Ming Liu
LiDAR-based localization is valuable for applications like mining surveys and underground facility maintenance. However, existing methods can struggle when dealing with uninformative geometric structures in challenging scenarios. This paper presents RELEAD, a LiDAR-centric solution designed to address scan-matching degradation. Our method enables degeneracy-free point cloud registration by solving constrained ESIKF updates in the front end and incorporates multisensor constraints, even when dealing with outlier measurements, through graph optimization based on Graduated Non-Convexity (GNC). Additionally, we propose a robust Incremental Fixed Lag Smoother (rIFL) for efficient GNC-based optimization. RELEAD has undergone extensive evaluation in degenerate scenarios and has outperformed existing state-of-the-art LiDAR-Inertial odometry and LiDAR-Visual-Inertial odometry methods.
Generalizing Unsupervised Lidar Odometry Model from Normal to Snowy Weather Conditions
Shanghai Polytechnic University（上海理工大学）；Singapore Management University（新加坡国立大学）；Nanjing University of Science and Technology（南京理工大学）；University of Macau（澳门大学）
LiDAR odometry can achieve accurate vehicle pose estimation for short driving range or in small-scale environments, but for long driving range or in large-scale environments, the accuracy deteriorates as a result of cumulative estimation errors. This drawback necessitates the inclusion of loop closure detection in a SLAM framework to suppress the adverse effects of cumulative errors. To improve the accuracy of pose estimation, we propose a new LiDAR-based SLAM method which uses F-LOAM as LiDAR odometry, Scan Context for loop closure detection, and GTSAM for global optimization. In our approach…
EFEAR-4D: Ego-Velocity Filtering for Efficient and Accurate 4D radar Odometry
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）；Research Institute of Intelligent Control and Systems, Harbin Institute of Technology（智能控制与系统研究院，哈尔滨工业大学）
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS)（测绘遥感信息工程国家重点实验室）；Wuhan University（武汉大学）；UCLA Samueli School of Engineering（加州大学洛杉矶分校塞缪利工程学院）
Monash University Indonesia（蒙纳士大学印度尼西亚校区）；University of Edinburgh（爱丁堡大学）
Ubiquitous positioning for pedestrian in adverse environment has served a long standing challenge. Despite dramatic progress made by Deep Learning, multi-sensor deep odometry systems yet pose a high computational cost and suffer from cumulative drifting errors over time. Thanks to the increasing computational power of edge devices, we propose a novel ubiquitous positioning solution by integrating state-of-the-art deep odometry models on edge with an EKF (Extended Kalman Filter)-LoRa backend. We carefully compare and select three sensor modalities, i.e., an Inertial Measurement Unit (IMU), a mi…
Doppler Correspondence: Non-Iterative Scan Matching With Doppler Velocity-Based Correspondence
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Republic of Korea（韩国乌山国立科学与技术研究院机械工程系）；Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Republic of Korea（韩国先进科学技术研究院机械工程系）
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）
PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping
Tsinghua University（清华大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；University of California, Berkeley（加州大学伯克利分校）；Xinchen Qihang Co., Ltd.（新晨启航有限公司）
Radar-Inertial Odometry with Online Spatio-Temporal Calibration via Continuous-Time IMU Modeling
University of Zagreb Faculty of Electrical Engineering and Computing, Laboratory for Autonomous Systems and Mobile Robotics (LAMOR)（泽格布大学电子工程与计算学院，自主系统与移动机器人实验室）；Calirad d.o.o.（Calirad公司）
AF-RLIO: Adaptive Fusion of Radar-LiDAR-Inertial Information for Robust Odometry in Challenging Environments
College of Information Engineering, Zhejiang University of Technology（浙江工业大学信息工程学院）；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）
University of Tokyo（东京大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Dynamic environments such as urban areas are still challenging for popular visual-inertial odometry (VIO) algorithms. Existing datasets typically fail to capture the dynamic nature of these environments, therefore making it difficult to quantitatively evaluate the robustness of existing VIO methods. To address this issue, we propose three contributions: firstly, we provide the VIODE benchmark, a novel dataset recorded from a simulated UAV that navigates in challenging dynamic environments. The unique feature of the VIODE dataset is the systematic introduction of moving objects into the scenes.…
