poseCovThreshold: 25.0 # m^2, threshold for using GPS data # Export settings savePCD: true # https://github.com/TixiaoShan/LIO-SAM/issues/3 savePCDDirectory: "/data/lio/" # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it....
A LiDAR scan of MulRan dataset has no ring information, thus we simply made a hardcoding likeint rowIdn = (i % 64) + 1in imageProjection.cpp to make a ring index information that LIO-SAM requires, and it works. However, if you use an other LiDAR, you need to change this line. C...
LiDAR-inertial SLAM: Scan Context + LIO-SAM. Contribute to gisbi-kim/SC-LIO-SAM development by creating an account on GitHub.
loam_velodyne放在ros工作空间之后,catkin_make之后,报错 解决方法:进入pcl_conversion 将其中的eigen路径由usr/include/eigen3改为usr/local/include/eigen3 git zheda分支使用方法 data_odometry_velodyne下只有一个dataset文件夹 (1)dataset文件夹下包括poese 和 sequences两个文件夹 (2) (2) velodyne文件夹下面 ...
该文章作为LeGO-LOAM作者的正统续作,也是近年来比较有了解价值的多传感器融合里程计,为此我们拿出来说一说。LIO-SAM实际上是LeGO-LOAM的扩展版本,添加了IMU预积分因子和GPS因子,去除了帧帧匹配部分。 论文认为loam系列文章存在一些问题:将其数据保存在全局体素地图中,难...
Thanks to LOAM, A-LOAM, and LIO-SAM code authors. The major codes in this repository are borrowed from their efforts. 代码:https://github.com/gisbi-kim/SC-A-LOAM 编译:点云PCL 本文仅做学术分享,如有侵权,请联系删除。 本文来自点云PCL博主的分享,未经作者允许请勿转载,欢迎各位同学积极分享和交...
该文章作为LeGO-LOAM作者的正统续作,也是近年来比较有了解价值的多传感器融合里程计,为此我们拿出来说一说。LIO-SAM实际上是LeGO-LOAM的扩展版本,添加了IMU预积分因子和GPS因子,去除了帧帧匹配部分。 论文认为loam系列文章存在一些问题:将其数据保存在全局体素地图中,难以执行闭环检测;没有结合其他绝对测量(GPS,指南针...
A Scan Context integration for LIO-SAM, namedSC-LIO-SAM (link), is also released. Real-time LiDAR SLAM: Scan Context (18 IROS) + LeGO-LOAM (18 IROS) This repository is an example use-case ofScan Context C++, the LiDAR place recognition method, for LiDAR SLAM applications. ...
A Scan Context integration for LIO-SAM, namedSC-LIO-SAM (link), is also released. Real-time LiDAR SLAM: Scan Context (18 IROS) + LeGO-LOAM (18 IROS) This repository is an example use-case ofScan Context C++, the LiDAR place recognition method, for LiDAR SLAM applications. ...
7.彻底剖析室内、室外激光SLAM关键算法原理、代码和实战(cartographer+LOAM +LIO-SAM) 8.从零搭建一套结构光3D重建系统[理论+源码+实践] 9.单目深度估计方法:算法梳理与代码实现 10.自动驾驶中的深度学习模型部署实战 11.相机模型与标定(单目+双目+鱼眼) ...