Although Structure-from-Motion (SfM) as a maturing technique has been widely used in many applications, state-of-the-art SfM algorithms are still not robust enough in certain situations. For example, images for inspection purposes are often taken in close distance to obtain detailed textures, which will result in less overlap between images and thus decrease the accuracy of estimated motion. In this paper, we propose a LiDAR-enhanced SfM pipeline that jointly processes data from a rotating LiDAR and a stereo camera pair to estimate sensor motions. We show that incorporating LiDAR helps to effectively reject falsely matched images and significantly improve the model consistency in large-scale environments. Experiments are conducted in different environments to test the performance of the proposed pipeline and comparison results with the state-of-the-art SfM algorithms are reported.
Wuhan University（武汉大学）；School of Computer Science（计算机学院）；School of Electronic Information（电子信息学院）；School of Artificial Intelligence（人工智能学院）；National Engineering Research Center for Multimedia Software（多媒体软件国家工程研究中心）；Institute for Math & AI（数学与人工智能研究所）
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）
A Real-time Degeneracy Sensing and Compensation Method for Enhanced LiDAR SLAM
Wuhan University（武汉大学）；State Key Laboratory of Surveying, Mapping and Remote Sensing（测绘遥感信息工程国家重点实验室）；Beijing Automation Control Equipment Research Institute（北京自动化控制设备研究所）
InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping
National Innovation Institute of Defence Technology, PLA Academy of Military Sciences（中国人民解放军军事科学院国防科技创新研究院）；PLA Information Engineering University（中国人民解放军信息工程大学）
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。
MDF: Multi-Modal Data Fusion with CNN-Based Object Detection for Enhanced Indoor Localization Using LiDAR-SLAM
Department of Artificial Intelligence Convergence, Pukyong National University（人工智能融合系，浦项国立大学）；School of Computer Science, University of Nottingham Ningbo China（计算机科学学院，诺丁汉国际大学）
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）
Adaptive Denoising-Enhanced LiDAR Odometry for Degeneration Resilience in Diverse Terrains
School of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Tongji Research Institute of Artificial Intelligence (Suzhou)（同济大学人工智能研究院（苏州））
PERAL: Perception-Aware Motion Control for Passive LiDAR Excitation in Spherical Robots
Centre for Advanced Robotics Technology Innovation (CARTIN), School of Electrical and Electronic Engineering, Nanyang Technological University（先进机器人技术创新中心（CARTIN）、电气与电子工程学院、南洋理工大学）
A Benchmark for Multi-Modal Lidar SLAM with Ground Truth in GNSS-Denied Environments
University of Turku（图尔库大学）
基于激光雷达的同时定位与地图构建（SLAM）方法在自主机器人系统中获得了相当大的成功。这在一定程度上是由于强大的SLAM算法的高精度以及新的低成本激光雷达产品的出现。这项研究对当前最先进的激光雷达SLAM算法进行了基准测试，其中多模态激光雷达传感器设置展示了不同的扫描模式（旋转和固态）和传感技术，以及安装在移动传感和计算平台上的激光雷达相机。我们扩展了我们以前的多模态多激光雷达数据集与额外的序列和新的来源的真值数据。具体而言，我们提出了一种新的多模态多激光雷达SLAM辅助和ICP为基础的传感器融合方法生成真值地图。有了这些地图，我们然后使用自然分布变换（NDT）方法匹配实时点云数据，以获得具有全6自由度位姿估计的真值。这种新颖的真值数据利用了高分辨率旋转和固态激光雷达。我们还包括新的开放道路序列与GNSS-RTK数据和额外的室内序列与运动捕捉（MOCAP）真值，补充以前的森林序列与MOCAP数据。我们进行了十个不同的SLAM算法和激光雷达组合实现的定位精度的分析。我们还报告了四种不同计算平台和总共五种设置（Intel和Jetson ARM CPU）的资源利用率。我们的实验结果表明，目前最先进的激光雷达SLAM算法执行不同类型的传感器非常不同。更多结果、代码和数据集可以在以下位置找到：\href{https://github.com/TIERS/tiers-lidars-datase…
I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry
Harbin Institute of Technology（哈尔滨工业大学）；Nanyang Technological University（南洋理工大学）；Hangzhou Guochen Robot Technology Company Limited（杭州国辰机器人科技有限公司）；Zhejiang University（浙江大学）
DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments
The University of Hong Kong（香港大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）；University of Macau（澳门大学）；D-Robotics；Zhejiang University（浙江大学）
Socially Aware Motion Planning for Service Robots Using LiDAR and RGB-D Camera
Fulbright University Vietnam（富布赖特越南大学）；Vietnam National University Hanoi（越南国家大学河内分校）；University of Technology Sydney（悉尼科技大学）；Le Quy Don University（黎贵敦大学）
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
Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization
Advanced Robotics and Automation (ARA) Laboratory（先进机器人与自动化实验室）；Computer Vision Laboratory (CVL)（计算机视觉实验室）；Evolutionary Computing Systems Lab, University of Nevada, Reno（内华达大学里诺分校进化计算系统实验室）
TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation
Research Center for Intelligent Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所智能计算系统研究中心）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；China North Artificial Intelligence & Innovation Research Institute（中国北方人工智能与创新研究院）；Collective Intelligence & Collaboration Laboratory (CIC)（集体智能与协作实验室）；Research Center for In（研究中心）
AWARE: Adaptive Whole-body Active Rotating Control for Enhanced LiDAR-Inertial Odometry under Human-in-the-Loop Interaction
organization= State Key Laboratory of Information Engineering in Surveying, Mapping；Remote Sensing , addressline= Wuhan University , city= Wuhan , postcode= 430079 , country= China；organization= School of Electrical；Electronic Engineering , addressline= Nanyang Technological University , postcode= 639798 , country= Singapore
3D Lidar Reconstruction with Probabilistic Depth Completion for Robotic Navigation
Oxford Robotics Institute, University of Oxford（牛津大学牛津机器人研究所）；Cluster of Excellence PhenoRob, Institute of Geodesy and Geoinformation, University of Bonn（波恩大学大地测量与地理信息研究所PhenoRob卓越集群）
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）
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）
RESPLE: Recursive Spline Estimation for LiDAR-Based Odometry
Department of Electrical Engineering, Linköping University（Linköping大学电子工程系）；Robotic Systems Lab (RSL), ETH Zurich（ETH Zurich机器人系统实验室）；Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen（Groningen大学伯努利学院）
