We introduce a LiDAR inertial odometry (LIO) framework, called LiPO, that enables direct comparisons of different iterative closest point (ICP) point cloud registration methods. The two common ICP methods we compare are point-to-point (P2P) and point-to-feature (P2F). In our experience, within the context of LIO, P2F-ICP results in less drift and improved mapping accuracy when robots move aggressively through challenging environments when compared to P2P-ICP. However, P2F-ICP methods require more hand-tuned hyper-parameters that make P2F-ICP less general across all environments and motions. In real-world field robotics applications where robots are used across different environments, more general P2P-ICP methods may be preferred despite increased drift. In this paper, we seek to better quantify the trade-off between P2P-ICP and P2F-ICP to help inform when each method should be used. To explore this trade-off, we use LiPO to directly compare ICP methods and test on relevant benchmark datasets as well as on our custom unpiloted ground vehicle (UGV). We find that overall, P2F-ICP has reduced drift and improved mapping accuracy, but, P2P-ICP is more consistent across all environments and motions with minimal drift increase.
SVN-ICP: Uncertainty Estimation of ICP-based LiDAR Odometry using Stein Variational Newton
Learning & Intelligent Systems Research Lab, TU Berlin（学习与智能系统研究实验室，柏林技术大学）；Technical University of Munich（慕尼黑技术大学）；Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI））
本文提出 SVN-ICP 算法，利用 Stein Variational Newton 在流形上实现基于 ICP 的激光雷达里程计不确定性估计，无需显式噪声建模，在多传感器融合中提供准确位姿估计和可靠不确定性。
Kinematic-ICP: Enhancing LiDAR Odometry with Kinematic Constraints for Wheeled Mobile Robots Moving on Planar Surfaces
Center for Robotics, University of Bonn（波恩大学机器人中心）；Dexory；Department of Engineering Science at the University of Oxford（牛津大学工程科学系）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
An Adaptive ICP LiDAR Odometry Based on Reliable Initial Pose
School of Information Science and Engineering, Wuhan University of Science and Technology（信息科学与工程学院，武汉科技大学）；School of Computer Science and Technology, Wuhan University of Science and Technology（计算机科学与技术学院，武汉科技大学）；School of Information Management and Institute of Big Data and Digital Economy, Hubei University of Economics（信息管理学院和大数据与数字经济研究院，湖北经济学院）
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）
BEV-LIO(LC): BEV Image Assisted LiDAR-Inertial Odometry with Loop Closure
School of Computer Science and Technology, Guangdong University of Technology（计算机科学与技术学院，广东技术大学）；School of Electrical and Electronic Engineering, Nanyang Technological University（电子与电气工程学院，南洋理工大学）
A Numerically-Robust ROS 2 Port of iG-LIO: Diagnosing and Fixing Toolchain-Induced Failures in Incremental GICP LiDAR-Inertial Odometry
Robotics Institute (RI), Carnegie Mellon University (CMU)（卡内基梅隆大学机器人研究所）；Institute of Systems and Robotics (ISR), University of Coimbra (UC)（科英布拉大学系统与机器人研究所）
Multi-Sensor Fusion for Quadruped Robot State Estimation using Invariant Filtering and Smoothing
Dynamic Legged Systems (DLS), Istituto Italiano di Tecnologia (IIT)（动态腿系统（DLS）、意大利技术研究院（IIT））；Korean Advanced Institute of Science and Technology (KAIST)（韩国先进科学研究院（KAIST））；Department of Engineering, Thompson Rivers University（工程系，汤普森河大学）
Unsigned Distance Maps on 2D Point Cloud Registration
Faculty of Engineering, University of Porto (FEUP)（波尔图大学工程学院）；INESC TEC – Institute for Systems and Computer Engineering, Technology and Science（INESC TEC – 系统工程与计算机科学、技术与科学研究所）；Sapienza University of Rome（罗马第一大学）；Flowbotic Mobile Systems, S.A.（Flowbotic移动系统公司）
P3-LOAM: PPP/LiDAR Loosely Coupled SLAM with Accurate Covariance Estimation and Robust RAIM in Urban Canyon Environment
Shanghai Jiao Tong University（上海交通大学）
基于激光雷达（LiDAR）的同时定位与地图构建（SLAM）技术在自动驾驶领域引起了越来越多的关注。然而，LiDAR-SLAM遭受累积误差，这可以通过全球导航卫星系统（GNSS）显著减轻。精密单点定位（PPP）是一种不依赖于基站的高精度GNSS工作模式，在无人系统中越来越受欢迎。针对LiDAR-SLAM和PPP这两种技术的特点，提出了一种将LiDAR-SLAM和PPP相结合的SLAM系统P3-LOAM（PPP based LiDAR Odometry and Mapping）。为了更好地集成，我们通过使用奇异值分解（SVD）雅可比模型推导LiDAR-SLAM定位协方差，因为SVD提供了迭代最近点（ICP）的显式解析解，这是LiDAR-SLAM中的关键问题。然后提出了一种新的方法来评估估计的LiDAR-SLAM协方差。此外，为了提高GNSS在城市峡谷环境中的可靠性，我们开发了一种LiDAR-SLAM辅助GNSS接收机自主完整性监测（RAIM）算法。最后，我们验证P $^3$ -LOAM与UrbanNav，一个具有挑战性的公共数据集在城市峡谷环境。综合测试结果表明，P3-LOAM在精度和可用性方面优于单点定位（SPP）、PPP、LeGO-LOAM、SPP-LOAM以及UrbanNav出版商提出的松耦合导航系统等基准。
Department of Electrical and Electronic Engineering, Imperial College London（帝国理工学院伦敦分校电子与电气工程系）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦德大学工程与物理科学学院）
Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes
Julius-Maximilians-Universität Würzburg（乌尔姆-约亨大学）；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）；University of Bonn（波恩大学）
HDA-LVIO: A High-Precision LiDAR-Visual-Inertial Odometry in Urban Environments with Hybrid Data Association
College of Intelligent Systems Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能系统科学与工程学院）；National Innovation Institute of Defense Technology, Academy of Military Sciences China（中国军事科学院国防科技创新研究院）；Tianjin Artificial Intelligence Innovation Center (TAIIC)（天津人工智能创新中心）
