Sensor calibration is the fundamental block for a multi-sensor fusion system. This paper presents an accurate and repeatable LiDAR-IMU calibration method (termed LI-Calib), to calibrate the 6-DOF extrinsic transformation between the 3D LiDAR and the Inertial Measurement Unit (IMU). %
Regarding the high data capture rate for LiDAR and IMU sensors, LI-Calib adopts a continuous-time trajectory formulation based on B-Spline, which is more suitable for fusing high-rate or asynchronous measurements than discrete-time based approaches. %
Additionally, LI-Calib decomposes the space into cells and identifies the planar segments for data association, which renders the calibration problem well-constrained in usual scenarios without any artificial targets. We validate the proposed calibration approach on both simulated and real-world experiments. The results demonstrate the high accuracy and good repeatability of the proposed method in common human-made scenarios. To benefit the research community, we open-source our code at \url{https://github.com/APRIL-ZJU/lidar_IMU_calib}
MFCalib: Single-shot and Automatic Extrinsic Calibration for LiDAR and Camera in Targetless Environments Based on Multi-Feature Edge
College of Mechatronics and Control Engineering, Shenzhen University（深圳大学机电与控制工程学院）；Peng Cheng Laboratory（鹏城实验室）；Manifold Tech Limited（万孚科技有限公司）；The University of Hong Kong（香港大学）
Multi-LiCa: A Motion and Targetless Multi LiDAR-to-LiDAR Calibration Framework
Institute of Automotive Technology（汽车技术研究所）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与智能机械研究所）；Technical University of Munich（慕尼黑技术大学）
Joint Optimization-based Targetless Extrinsic Calibration for Multiple LiDARs and GNSS-Aided INS of Ground Vehicles
Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology（系统工程研究院和智能科学与系统联合实验室，澳门科学技术大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院（AIR），清华大学）
UniCalib: Targetless LiDAR-Camera Calibration via Probabilistic Flow on Unified Depth Representations
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Electronic Information, Wuhan University（武汉大学电子信息学院）；School of Artificial Intelligence, Wuhan University（武汉大学人工智能学院）
Target-free Extrinsic Calibration of a 3D-Lidar and an IMU
Texas A&M University（得克萨斯农工大学）；Ford Motor Company（福特汽车公司）
本文提出了一种新的无目标外部标定算法，用于三维激光雷达和IMU对，使用扩展卡尔曼滤波器（EKF），利用\textit{motion based calibration constraint}进行状态更新。这些步骤包括，通过激光雷达惯性传感器套件沿所有自由度的运动激励来数据汇集，通过在最小二乘优化框架中使用上述\textit{motion based calibration constraint}的旋转分量来确定传感器间旋转，以及最后，使用\textit{motion based calibration constraint}来确定传感器间平移在扩展的卡尔曼滤波器（EKF）框架中进行状态更新。我们使用实验室收集的数据和开源（https：//github.com/unmannedlab/imu_lidar_calibration）我们对机器人研究社区的贡献来实验验证我们的方法。
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
The Space Robotics Lab. in the Department of Aerospace Engineering, Graduate School of Engineering, Tohoku University（东京大学工学研究生院航空航天工程系空间机器人实验室）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
LiDAR-Inertial Odometry Based on Extended Kalman Filter
Institute of Innovation for Future Society, Nagoya University（名古屋大学未来社会创新研究所）；LOCT Co., Ltd.（LOCT有限公司）；Graduate School of Science and Technology, University of Tsukuba（筑波大学理工学研究科）
FF-LINS: A Consistent Frame-to-Frame Solid-State-LiDAR-Inertial State Estimator
GNSS Research Center, Wuhan University（武汉大学GNSS研究中心）；Hubei Luojia Laboratory（湖北珞珈实验室）；Artificial Intelligence Institute of Wuhan University（武汉大学人工智能研究院）
Large-Scale Tunnel Air-Ground Collaboration With FLISP: Fast LiDAR-IMU Synchronized Path Planner
Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University（同济大学上海自主智能无人系统科学中心）；State Key Laboratory of Autonomous Intelligent Unmanned Systems, Tongji University（同济大学自主智能无人系统全国重点实验室）；College of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）
WinTA-GIL: Windowed Trajectory Alignment for GNSS-IMU-LiDAR Heading Refinement in Intermittent Signal Environments
State Key Laboratory 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）；School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；College of Engineering and Computer Science, University of Michigan-Dearborn（美国密歇根大学迪尔伯恩分校工程与计算机科学学院）
Low-Cost GPS-Aided LiDAR State Estimation and Map Building
Shenzhen Unity-Drive Innovation Technology Co. Ltd.（深圳一清创新科技有限公司）；Hong Kong University of Science and Technology（香港科技大学）；Hangzhou ATG Intelligent Technology Co. Ltd.（杭州ATG智能科技有限公司）
Adaptive-LIO: Enhancing Robustness and Precision through Environmental Adaptation in LiDAR Inertial Odometry
State Key Laboratory of Robotics and Systems（机器人学与系统国家重点实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Hangzhou Qisheng Intelligent Techology Co. Ltd.（杭州齐盛智能科技有限公司）；School of Mechatronic Engineering, China University of Mining and Technology（中国矿业大学机电工程学院）；School of Mechanical and Electrical Engineering, University of Electronic Science And Technology（电子科技大学机械与电气工程学院）；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 of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
