过去几十年间，研究人员已开发出大量激光雷达惯性里程计（LiDAR-inertial odometry, LIO）算法，在各类环境中均展现出令人满意的性能。但这类算法大多仅在开阔户外环境中得到验证，在受限室内场景中往往会面临挑战。此类室内环境中，激光雷达扫描变化快，且存在墙体、楼梯等重复结构特征，在多层建筑中尤为突出，导致可靠的点云配准难以实现。本文提出NV-LIO，这是一种基于法向量的LIO框架，专为含多层结构的室内环境下的同时定位与建图（simultaneous localization and mapping, SLAM）设计。我们的方法从激光雷达扫描中提取法向量，并将其用于对应点搜索以提升点云配准性能。为保证配准鲁棒性，我们分析了法向量方向的分布，同时研究退化情形以调整匹配不确定性。此外，我们还实现了一个基于视角的回环检测模块，以避免被墙体遮挡导致的错误匹配。我们通过公开数据集和自制数据集验证了所提方法的有效性。为回馈社区，代码将在https://github.com/dhchung/nv_lio 上公开。
英文摘要：
Over the last few decades, numerous LiDAR-inertial odometry (LIO) algorithms have been developed, demonstrating satisfactory performance across diverse environments. Most of these algorithms have predominantly been validated in open outdoor environments, however they often encounter challenges in confined indoor settings. In such indoor environments, reliable point cloud registration becomes problematic due to the rapid changes in LiDAR scans and repetitive structural features like walls and stairs, particularly in multifloor buildings. In this paper, we present NV-LIO, a normal vector based LIO framework, designed for simultaneous localization and mapping (SLAM) in indoor environments with multifloor structures. Our approach extracts the normal vectors from the LiDAR scans and utilizes them for correspondence search to enhance the point cloud registration performance. To ensure robust registration, the distribution of the normal vector directions is analyzed, and situations of degeneracy are examined to adjust the matching uncertainty. Additionally, a viewpoint based loop closure module is implemented to avoid wrong correspondences that are blocked by the walls. The propsed method is validated through public datasets and our own dataset. To contribute to the community, the code will be made public on https://github.com/dhchung/nv_lio.
Simultaneous Localization and Mapping (SLAM) is considered to be an essential capability for intelligent vehicles and mobile robots. However, most of the current lidar SLAM approaches are based on the assumption of a static environment. Hence the localization in a dynamic environment with multiple moving objects is actually unreliable. The paper proposes a dynamic SLAM framework RF-LIO, building on LIO-SAM, which adds adaptive multi-resolution range images and uses tightly-coupled lidar inertial odometry to first remove moving objects, and then match lidar scan to the submap. Thus, it can obta…
Massachusetts Institute of Technology（麻省理工学院）；Stevens Institute of Technology（斯蒂文斯理工学院）
We propose a framework for tightly-coupled lidar inertial odometry via smoothing and mapping, LIO-SAM, that achieves highly accurate, real-time mobile robot trajectory estimation and map-building. LIO-SAM formulates lidar-inertial odometry atop a factor graph, allowing a multitude of relative and absolute measurements, including loop closures, to be incorporated from different sources as factors into the system. The estimated motion from inertial measurement unit (IMU) pre-integration de-skews point clouds and produces an initial guess for lidar odometry optimization. The obtained lidar odomet…
LIO-EKF: High Frequency LiDAR-Inertial Odometry using Extended Kalman Filters
Institute of Geodesy and Geoinformation, University of Bonn（波恩大学大地测量与地理信息研究所）；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）
SR-LIO: LiDAR-Inertial Odometry with Sweep Reconstruction
Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；Huazhong University of Science and Technology（华中科技大学）
AC-LIO: Towards Asymptotic Compensation for Distortion in LiDAR-Inertial Odometry via Selective Intra-Frame Smoothing
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, China（信息工程测绘遥感国家重点实验室（LIESMARS），武汉大学，中国）；Department of Mechanical Engineering, University of Michigan-Dearborn（密歇根大学迪尔伯恩分校机械工程系）；Electronic Information School, Wuhan University, China（武汉大学电子信息学院，中国）；School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子电气工程学院，新加坡）
This paper presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various …
Zhejiang University（浙江大学）；Hangzhou Normal University（杭州师范大学）
With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is de…
University of the Balearic Islands（巴利阿里群岛大学）；Institut d’Investigacio Sanitaria Illes Balears（巴利阿里群岛卫生研究所）；Hospital Universitario Son Espases（松埃斯帕塞斯大学医院）
In the last decades, Light Detection And Ranging (LiDAR) technology has been extensively explored as a robust alternative for self-localization and mapping. These approaches typically state ego-motion estimation as a non-linear optimization problem dependent on the correspondences established between the current point cloud and a map, whatever its scope, local or global. This paper proposes LiODOM, a novel LiDAR-only ODOmetry and Mapping approach for pose estimation and map-building, based on minimizing a loss function derived from a set of weighted point-to-line correspondences with a local m…
BIEVR-LIO: Robust LiDAR-Inertial Odometry through Bump-Image-Enhanced Voxel Maps
1 Autonomous Systems Lab, ETH Z\"urich, Switzerland, 2 Robotic Systems Lab, ETH Z\"urich, Switzerland, 3 Mobile Robotics Lab, ETH Z\"urich, Switzerland -1.5mm
This paper presents FAST-LIO2: a fast, robust, and versatile LiDAR-inertial odometry framework. Building on a highly efficient tightly-coupled iterated Kalman filter, FAST-LIO2 has two key novelties that allow fast, robust, and accurate LiDAR navigation (and mapping). The first one is directly registering raw points to the map (and subsequently update the map, i.e., mapping) without extracting features. This enables the exploitation of subtle features in the environment and hence increases the accuracy. The elimination of a hand-engineered feature extraction module also makes it naturally adap…
Computational Control Engineering Laboratory (CoCEL), Department of Convergence IT Engineering and Electrical Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, South Korea（convergence 信息技术工程与电气工程系 计算控制工程实验室，POSTECH 首尔大学（Pohang University of Science and Technology）, Pohang 37673, South Korea）；Laboratory for Information & Decision Systems (LIDS), Massachusetts Institute of Technology, Cambridge, MA 02139, USA（信息与决策系统实验室，麻省理工学院（Massachusetts Institute of Technology）, Cambridge, MA 02139, USA）
LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map
Centre for Advanced Robotics Technology Innovation (CARTIN)（先进机器人技术创新中心）；School of Electrical and Electronic Engineering（电气与电子工程学院）；Nanyang Technological University（南洋理工大学）
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）
Super-LIO: A Robust and Efficient LiDAR-Inertial Odometry System with a Compact Mapping Strategy
Department of Mechanical Engineering, East China University of Science and Technology（东华大学机械工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Hong Kong（香港大学）
The University of Hong Kong（香港大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））
Accurate self and relative state estimation are the critical preconditions for completing swarm tasks, e.g., collaborative autonomous exploration, target tracking, search and rescue. This paper proposes Swarm-LIO: a fully decentralized state estimation method for aerial swarm systems, in which each drone performs precise ego-state estimation, exchanges ego-state and mutual observation information by wireless communication, and estimates relative state with respect to (w.r.t.) the rest of UAVs, all in real-time and only based on LiDAR-inertial measurements. A novel 3D LiDAR-based drone detectio…
AKF-LIO: LiDAR-Inertial Odometry with Gaussian Map by Adaptive Kalman Filter
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；International Digital Economy Academy（国际数字经济学院）；The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）
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）
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）
Institute for Infocomm Research, A-STAR（新加坡科技研究局信息通信研究院）；Nanyang Technological University（南洋理工大学）
Light detection and ranging (LiDAR) on a moving agent could suffer from motion distortion due to simultaneous rotation of the LiDAR and fast movement of the agent. An accurate piecewise linear de skewing algorithm is proposed to correct the motion distortions for LiDAR inertial odometry (LIO) using high frequency motion information provided by an Inertial Measurement Unit (IMU). Experimental results show that the proposed algorithm can be adopted to improve the performance of existing LIO algorithms especially in cases of fast movement.
LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis
School of Surveying and Geo-Informatics, Tongji University（同济大学测量与地理信息学院）；School of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Institute of Intelligent Vehicles, Tongji University（同济大学智能车辆研究所）
University of California Los Angeles（加利福尼亚大学洛杉矶分校）
Aggressive motions from agile flights or traversing irregular terrain induce motion distortion in LiDAR scans that can degrade state estimation and mapping. Some methods exist to mitigate this effect, but they are still too simplistic or computationally costly for resource-constrained mobile robots. To this end, this paper presents Direct LiDAR-Inertial Odometry (DLIO), a lightweight LiDAR-inertial odometry algorithm with a new coarse-to-fine approach in constructing continuous-time trajectories for precise motion correction. The key to our method lies in the construction of a set of analytica…
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）
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）
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）
SR-LIO++: LiDAR-Inertial Odometry and Quantized Mapping with Caching-Aware Sweep Reconstruction
ACCESS-AI Chip Center for Emerging Smart Systems, InnoHK Centers, Hong Kong Science Park, Hong Kong, China（新兴智能系统 ACCESS-AI 芯片中心，InnoHK 中心，香港科学公园，香港，中国）；HongKong University of Science and Technology, HongKong, China（香港理工大学，香港，中国）；Huazhong University of Science and Technology, Wuhan, China（华中科技大学，武汉，中国）；Hangzhou Qisheng Intelligent Techology Co. Ltd., Hangzhou, China（杭州启盛智能科技有限公司，杭州，中国）；Southeast University, Nanjing, China（东南大学，南京，中国）
Terrain-Awared LiDAR-Inertial Odometry for Legged-Wheel Robots Based on Radial Basis Function Approximation
School of Automation and Intelligent Sensing, Institute of Medical Robotics, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education（自动化与智能感知学院、医学机器人研究院、上海交通大学，系统控制与信息处理重点实验室）
KN-LIO: Geometric Kinematics and Neural Field Coupled LiDAR-Inertial Odometry
School of Electronics, Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；School of Information and Control Engineering, China University of Mining and Technology（中国矿业大学信息与控制工程学院）
LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection
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）
To achieve accurate and robust pose estimation in Simultaneous Localization and Mapping (SLAM) task, multi-sensor fusion is proven to be an effective solution and thus provides great potential in robotic applications. This paper proposes FAST-LIVO, a fast LiDAR-Inertial-Visual Odometry system, which builds on two tightly-coupled and direct odometry subsystems: a VIO subsystem and a LIO subsystem. The LIO subsystem registers raw points (instead of feature points on e.g., edges or planes) of a new scan to an incrementally-built point cloud map. The map points are additionally attached with image…
We propose an accurate and robust multi-modal sensor fusion framework, MetroLoc, towards one of the most extreme scenarios, the large-scale metro vehicle localization and mapping. MetroLoc is built atop an IMU-centric state estimator that tightly couples light detection and ranging (LiDAR), visual, and inertial information with the convenience of loosely coupled methods. The proposed framework is composed of three submodules: IMU odometry, LiDAR-inertial odometry (LIO), and Visual-inertial odometry (VIO). The IMU is treated as the primary sensor, which achieves the observations from LIO and VI…
RAM-LAB（机器人与自动化实验室）；Hong Kong University of Science and Technology（香港科技大学）
Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constraine…
