gaoxiang12/slam_in_autonomous_driving resource

Health v2 · maintenance only

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2569 stars · 584 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Companion source code for the book 'SLAM in Autonomous Driving', implementing LiDAR SLAM, LiDAR-inertial odometry, and integrated navigation algorithms in C++. It covers ESKF, pre-integration, 2D/3D LiDAR mapping, NDT, ICP, and fusion localization with educational implementations.

Use cases

• learn lidar slam from scratch
• implement error-state kalman filter for imu gnss fusion
• build 2d and 3d lidar mapping algorithms
• understand lidar inertial odometry tightly coupled
• study ndt and icp point cloud registration
• offline mapping and loop closure detection example
• ekf fusion localization with lidar maps

When to choose

• you are studying SLAM for autonomous driving and want book-aligned, pedagogical implementations
• you want to implement classic algorithms like ESKF, pre-integration, NDT, and ICP yourself
• you need reference C++ code for LiDAR-inertial odometry and mapping pipelines

When to avoid

• you need a production-ready, well-supported SLAM library with a maintained license
• you want a plug-and-play solution rather than educational code tied to a book
• you require a permissively licensed dependency - the repository has no license

Facets

learning-resource · maturity active

simulation math computer-vision developer-tools autonomous-vehicles robotics tutorials cpp cross-platform slam lidar lidar-inertial-odometry kalman-filter point-cloud ndt icp gnss imu autonomous-driving book-companion-code algorithms linux

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