Estimation of Closest In-Path Vehicle (CIPV) by Low-Channel LiDAR and Camera Sensor Fusion for Autonomous Vehicle
College of Interdisciplinary（交叉学院）；Robotics Engineering（机器人工程学院）；Interdisciplinary Engineering, Daegu Gyeongbuk Institute of Science and Technology(DGIST)（大邱庆北科学技术院交叉工程学院）
In autonomous driving, using a variety of sensors to recognize preceding vehicles in middle and long distance is helpful for improving driving performance and developing various functions. However, if only LiDAR or camera is used in the recognition stage, it is difficult to obtain necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the tracking data of vision into bird's eye view (BEV) coordinates using an equation that projects LiDAR points onto an image, and a method of fusion between LiDAR and vision tracked data. Thus, the newly proposed method was effective through the results of detecting closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance is improved through improved cognitive performance than when using only LiDAR. In experimental results, the performance of the proposed method was proved through actual vehicle tests in various scenarios. Consequently, it is convincing that the newly proposed sensor fusion method significantly improves the ACC function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.
Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs
Centro Universitario de la Defensa, Zaragoza（萨拉戈萨国防大学中心）；Instituto de Investigación en Ingeniería de Aragón（阿拉贡工程研究院）；University of Zaragoza（萨拉戈萨大学）；Investigación en Ingeniería de Aragón（阿拉贡工程研究院）
Transforming Omnidirectional RGB-LiDAR data into 3D Gaussian Splatting
Department of Computer Science, State University of New York（计算机科学系，纽约州立大学）；Department of Mechanical Engineering, State University of New York（机械工程系，纽约州立大学）
Moving Object Localization based on the Fusion of Ultra-WideBand and LiDAR with a Mobile Robot
Singapore University of Technology and Design（新加坡科技设计大学）；Nanyang Technological University（南洋理工大学）；Southwest University of Science and Technology（西南科技大学）
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。
Semantic localization on BIM-generated maps using a 3D LiDAR sensor
Hong Kong University of Science and Technology（香港科技大学）；National University of Singapore（新加坡国立大学）；China Mobile (Zhejiang) Innovation Research Institute Co., Ltd（中国移动（浙江）创新研究院有限公司）
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。
GMMCalib: Extrinsic Calibration of LiDAR Sensors using GMM-based Joint Registration
Technical University of Munich（慕尼黑工业大学）；TUM School of Engineering and Design（慕尼黑工业大学工程与设计学院）；TUM School of Computation, Information and Technology（慕尼黑工业大学计算、信息与技术学院）
Towards Robust LiDAR Localization: Deep Learning-based Uncertainty Estimation
Department of Computer Engineering, Amirkabir University of Technology（阿姆irkabir技术大学计算机工程系）；Graduate School of Information Science and Technology, The University of Tokyo（东京大学信息科学与技术研究生院）
Terrain Perception for Agricultural UAVs in Complex Farmland via Rotating mmWave Radar
School of Computer Science, Hangzhou Dianzi University（杭州电子科技大学计算机科学学院）；TopXGun Robotics（TopXGun机器人）；CARDC；College of Control Science and Engineering, Zhejiang University（浙江大学控制科学与工程学院）；College of Electronics and Information Engineering, Tongji University（同济大学电子信息工程学院）
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）
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）
RMGS-SLAM: Real-time Multi-sensor Gaussian Splatting SLAM
Advanced Robotics Centre, National University of Singapore（新加坡国立大学先进机器人中心）；School of Aeronautics and Astronautics, Shanghai Jiao Tong University（上海交通大学航空宇航学院）；College of Computer Science, Sichuan University（四川大学计算机学院）；Singapore-MIT Alliance for Research and Technology（新加坡-麻省理工联合研究技术联盟）
