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...
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:http://arxiv.org/abs/2403.02280 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。
标题:LVI-GS: Tightly-coupled LiDAR-Visual-Inertial SLAM using 3D Gaussian Splatting 作者:Huibin Zhao, Weipeng Guan, Peng Lu 机构:The University of Hong Kong 原文链接:https://arxiv.org/abs/2411.02703 1. 导读 3D Gaussian Splatting (3DGS)在快速渲染和高保真映射方面显示了它的能力。在本文中,我们...
标题:Super Odometry: IMU-centric LiDAR-Visual-Inertial Estimator for Challenging Environments 作者:Shibo Zhao, Hengrui Zhang, Peng Wang, Lucas Nogueira, Sebastian Scherer 来源:ICRA 2021 编译:廖邦彦 审核:王志勇 本文转载自泡泡机器人SLAM,文章仅用于学术分享。 摘要 我们提出了Super Odometry,一个高精度的...
Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness.Liu,...
Existing LiDAR-inertial-visual odometry and mapping (LIV-SLAM) systems mainly utilize the LiDAR-inertial odometry (LIO) module for structure reconstruction and the visual-inertial odometry (VIO) module for color rendering. However, the accuracy of VIO is often compromised by photometric changes, weak...
LiDAR-Inertial-Visual Odometry发布于 2022-03-17 19:39 · 4044 次播放 赞同4添加评论 分享收藏喜欢 举报 视觉里程计(Visual Odometry)同时定位和地图构建(SLAM)计算机视觉 写下你的评论... 还没有评论,发表第一个评论吧
[Remote Sensing2024] LVI-Fusion: A Robust Lidar-Visual-Inertial SLAM Scheme [paper] 2023[TPAMI2023] SDV-LOAM: Semi-Direct Visual–LiDAR Odometry and Mapping [paper][code] [RAL2023] Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline [paper][co...
介绍 提出了一个低代价双目视觉惯导定位系统,实现了基于多状态约束下的卡尔曼滤波器(MSCKF)VIO,采用了先验雷达地图。除了稀疏的视觉特征,雷达地图与半稠密的点云也通过紧耦合的MSCKF进行更新,进而可以纠正漂移。点云和视觉之间的跨模态限制对VIO系统有改善作用。 总之
《LIV-GaussMap: LiDAR-Inertial-Visual Fusion for Real-time 3D Radiance Field Map Rendering》(hhttps://arxiv.org/pdf/2401.14857v1.pdf)介绍了一种集成的精确激光雷达、惯性和视觉(LIV)多模态传感器融合映射系统,该系统基于可微表...