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Code exercises for the SLAM course in 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' lecture series

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fastcampus_slam_codes

This repository contains code exercises for the SLAM section in the lecture series - 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' at FastCampus. This lecture series is delivered in Korean language.

How to use

Most of the code exercises are based on the base docker image. The base docker image contains numerous C++ libraries for SLAM, such as OpenCV, Eigen, Sophus, PCL, and ceres-solver.

You can build the base docker image using the following command.

docker build . --tag slam:latest --progress=plain
echo "xhost +local:docker" >> ~/.profile

Table of contents

Acknowledgements

ORB-SLAM 2/3 authors, DynaVINS authors, CubeSLAM authors, HDL-Graph-SLAM authors, KISS-ICP authors, SHINE-Mapping authors, and all the authors of the libraries used in this repository.

Contributors

Thanks goes to these wonderful people ❤️:


Juwon Jason Kim

:octocat:

Hyungtae Lim

:octocat:

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Code exercises for the SLAM course in 'Computer Vision, LiDAR processing, and Sensor Fusion for Autonomous Driving' lecture series

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