A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on Quatro and Nano-GICP - engcang/FAST-LIO-SAM-QN
fast-lio-sam https://github.com/hku-mars/FAST_LIOgithub.com/hku-mars/FAST_LIO FAST-LIO,FAST-LIO2与FASTER-LIO - 古月居www.guyuehome.com/38613 综述 由于在ikd-tree上计算效率的提高,我们直接将原始点配准到地图上,这使得帧间配准即使是在剧烈的运动和非常混乱的环境中也准确可靠。我们称这种基...
# terminal 1: run FAST-LIO2 mkdir -p ~/catkin_fastlio_slam/src cd ~/catkin_fastlio_slam/src git clone https://github.com/JzHuai0108/FAST_LIO_SLAM.git git clone https://github.com/Livox-SDK/livox_ros_driver cd .. catkin build -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE...
git clone https://github.com/hku-mars/FAST_LIO.git# 如果上面的GitHub链接在clone过程中常报错,可以使用下面的git clone https://gitee.com/Rormen/FAST_LIO 2 更新一下 FAST_LIO中的子模块 cdFAST_LIO git submodule update --init 这个过程中,很可能报错。(git@github.com: Permission denied) 解决办法...
参考https://github.com/hku-mars/FAST_LIO Livox-sdk livox_ros_driver FAST_LIO 运行demo 简要说明 前面说过,fast_lio是一种里程计算法,他可以根据输入的传感器的数据,输出机器人的里程计信息等。如下图所示,fast_lio文件下的launch文件夹下,共计有四种雷达的launch文件,分别为avia、horizon、outer64、velodyne...
开源代码(待上传):https://github.com/hku-mars/FAST-LIVO 本文的贡献如下: 1、一种紧耦合的LiDAR-inertial-visual里程仪框架,它建立在两个紧耦合的里程计系统之上:LIO子系统和VIO子系统,这两个子系统都不需要提取特征,通过将各自的激光雷达或视觉数据与IMU进行融合来联合估计系统状态。
cd ~/catkin_ws/src git clone https://github.com/XW-HKU/fast_lio.git cd .. catkin_make source devel/setup.bash 3. Directly run 3.1 For indoor environments and high frame-rate (such as 100hz) Connect to your PC to Livox Avia LiDAR by followingLivox-ros-driver installation, then ...
06 Fast-LIO testing-2023-08-27_15.09.40 01:58 lio sam testing-2023-08-27_11.23.16 02:23 Fast-LIO testing-2023-08-27_15.19.38 03:17 Fast-LIO testing-2023-08-27_15.05.45 01:01 lio sam testing-2023-08-27_11.08.28 02:15 lio sam testing-2023-08-27_11.18.25 02:06 lio sam ...
Fast Gaussian: 提出快速高斯溅射方法，论文为arxiv.org/abs/2403.1024...。FastClip: 提出高效视频理解系统，论文为dl.acm.org/doi/10.1145/...。FAST-LIO: 提出快速、稳健的LiDAR惯性里程计套件，论文为arxiv.org/abs/2010.0819...。FAST-LIO2: 提出快速直接激光雷达惯性里程计，论文为arxiv....
特征点配准最主流的做法是提取特征点,然后优化特征点的 point-to-line/surface 距离,如 LOAM/LeGO-LOAM/Lio-SAM,以及基于深度学习提取特征的 CAE-LO 等,但深度学习的方法受限于特征必须要事先学习。 Methodology 方法 A. Sensor Model and Feature Extraction 特征提取 ...