# gaoxiang12/slam_in_autonomous_driving

《自动驾驶中的SLAM技术》对应开源代码

Repository: https://github.com/gaoxiang12/slam_in_autonomous_driving
Canonical: https://ross.abutalabs.com/products/slam_in_autonomous_driving
Language: C++
License Family: other
Last push: 2025-11-13T06:14:24+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 52, release rhythm 35, longevity 92
- inputs: {"age_days": 1300, "days_push": 293, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2569, forks 584 (observed 2026-08-28T04:07:01.296366+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: simulation, math, computer-vision, developer-tools
- domain: autonomous-vehicles, robotics, tutorials
- platform: cpp, cross-platform
- tags: slam, lidar, lidar-inertial-odometry, kalman-filter, point-cloud, ndt, icp, gnss, imu, autonomous-driving, book-companion-code, algorithms, linux

## Member repositories
- gaoxiang12/slam_in_autonomous_driving (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:01.296366+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:23:20.499985+00:00, confidence not recorded.
  - readme: https://github.com/gaoxiang12/slam_in_autonomous_driving (fetched 2026-08-28T04:07:01.296366+00:00, sha d92eb1361049)
- Data as of 2026-08-30T08:39:29.467469+00:00.