最近想学习一下无人驾驶SLAM方面的内容

代码测试

这里先基于kitti数据集，进行测试。之前博客中已经介绍过kitti数据集了。本博文就用这个数据集来进行各种经典方法的复现

VINS-FUSION

把vins-mono也配置一下好了~

GitHub - qintonguav/VINS-MonoContribute to qintonguav/VINS-Mono development by creating an account on GitHub.https://github.com/qintonguav/VINS-Mono

vins系列真的非常丰富，下面还有个co-vins

https://github.com/qintonguav/Co-VINShttps://github.com/qintonguav/Co-VINS

A-LOAM

GitHub - HKUST-Aerial-Robotics/A-LOAM: Advanced implementation of LOAMAdvanced implementation of LOAM. Contribute to HKUST-Aerial-Robotics/A-LOAM development by creating an account on GitHub.https://github.com/HKUST-Aerial-Robotics/A-LOAM

LIO-Mapping:A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach

GitHub - hyye/lio-mapping: Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)https://github.com/hyye/lio-mapping

AVP-SLAM: Semantic Visual Mapping and Localization for Autonomous Vehicles in the Parking Lot

GitHub - qintonguav/AVP-SLAM-SIM: A basic implementation(not official code) of AVP-SLAM(IROS 2020) in simulation. https://arxiv.org/abs/2007.01813https://github.com/qintonguav/AVP-SLAM-SIM

ORB-SLAM 跑kitti

GVINS: Tightly Coupled GNSS-Visual-Inertial Fusion for Smooth and Consistent State Estimation

GitHub - HKUST-Aerial-Robotics/GVINS: Tightly coupled GNSS-Visual-Inertial system for locally smooth and globally consistent state estimation in complex environment.https://github.com/HKUST-Aerial-Robotics/GVINS

SuMa++: Efficient LiDAR-based Semantic SLAM

​​​​​​https://github.com/PRBonn/semantic_sumahttps://github.com/PRBonn/semantic_suma

DSO: Direct Sparse Odometry

DSO跑KITTI数据集_SLAM的博客-CSDN博客安装DSO此步非常简单，安装也很快，不详述，【参考博客】或【GitHub源网页】写在前面数据集中的camera.txt文件是可以更改的，且需要改成对应的相机参数图片的大小可以不用更改为一般的1280x1024，就保持KITTI数据集的图片尺寸就行图片的扩展名不用更改times.txt文件也可以不自己做（取巧的办法）可以不用压缩包直接解压选择图片文件夹就行需要注意的问题首先可...https://blog.csdn.net/weixin_43166819/article/details/103133570

DSO安装与调试 - huicanlin - 博客园

OverlapNet - Loop Closing for 3D LiDAR-based SLAM

https://github.com/PRBonn/OverlapNet

High-speed Autonomous Drifting with Deep Reinforcement Learning

GitHub - caipeide/drift_drl: High-speed Autonomous Drifting with Deep Reinforcement Learning

https://sites.google.com/view/autonomous-drifting-with-drl

参考资料