说回正题,为什么叫“一套真正可以用的硬同步LiDAR-Inertial-Visual便携手持三维重建系统”,因为整套系统的软硬件稳定性+同步精度+手持的舒适性我们已经反复验证多次,累计采集且跑得还不错有1000G的数据了,是一套经得起考验,反复打磨过的硬件系统。 图1. 整个手持系统的overview(a)实物图 (b)CAD模型图 (c)硬同步...
Extensive experiments are performed on both the public NTU dataset and the private handheld dataset, and the results show that the proposed FT-LVIO outperforms the state-of-the-art LiDAR-inertial, visual-inertial and LiDAR-visual-inertial methods in both accuracy and robustness. Furthermore, FT-...
ICRA 2024 | 紧耦合的Lidar-Visual-Inertial SLAM和大尺度体积占有率建图 【Tightly-Coupled LiDAR-Visual-Inertial SLAM and Large-Scale Volumetric Occupancy Mapping】 文章链接:arxiv.org/abs/2403.0228 作者单位:慕尼黑工业大学、帝国理工学院 自主导航是移动机器人在现实世界中每一个潜在应用的关键需求之一。除了高...
我们提出了一种完全紧耦合的LiDAR -视觉-惯性SLAM系统和应用局部子映射策略的三维建图框架,以实现对大规模环境的可扩展性。 引入了一种新颖的、无对应关系的、固有概率的LiDAR残差公式,仅用占有率场及其各自的梯度来表示。这些残差可以被添加到因子图优化问题中,或者作为实时估计的帧到图因子,或者作为子图之间相互对齐...
LiDAR-Inertial-Visual (LIV) sensor configuration has demonstrated superior performance in localization and dense mapping by leveraging complementary sensing characteristics: rich texture information from cameras, precise geometric measurements from LiDAR, and high-frequency motion data from IMU. Inspired by ...
A LiDAR-Inertial-Visual SLAM with Enhanced Dual-Subsystem for Limited FoV LiDAR, 视频播放量 156、弹幕量 0、点赞数 4、投硬币枚数 0、收藏人数 10、转发人数 3, 视频作者 bili_54523729337, 作者简介 ,相关视频:这是Lidar or 3DGS?,[RA-L 2025]去除激光SLAM建图的
"R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package." [2] Xu, Wei, et al. "Fast-lio2: Fast direct lidar-inertial odometry." [3] Lin, Jiarong, et al. "R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-...
hku-mars/r2live R2LIVE A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping Our preprint paper: we have corrected some typos and errors of our previous version of paper, the amended paper can be access athere. When amending our paper, I would like to ...
VIS系统跟踪视觉特征，LIS系统提取激光雷达特征进行扫描匹配，两者相互促进。LVI-SAM在单目相机下运行，能够处理各种环境，包括弱纹理区域。系统概述展示了由激光雷达、单目相机和IMU构成的输入处理流程，VIS与LIS子系统分别负责视觉与激光雷达数据的融合。LVI-SAM通过优化IMU、视觉里程计、激光雷达里程计以及闭环...
LVISAM论文的核心内容如下：系统框架：LVISAM框架结合了激光雷达、视觉和惯性单元，实现了实时定位和地图构建。系统分为视觉惯性系统与激光雷达惯性系统，两者通过平滑与建图优化协同工作。子系统功能：VIS系统负责跟踪视觉特征。LIS系统负责提取激光雷达特征并进行扫描匹配，与VIS系统相互促进，提升定位精度。系统...