Slam_in_autonomous_driving

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slam_in_autonomous_driving is an Open Source tool that provides the source code for SLAM technology in autonomous driving. It accompanies a book and offers resources for inertial navigation, LiDAR mapping, and LiDAR-inertial odometry.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is slam_in_autonomous_driving?

slam_in_autonomous_driving is an open-source repository offering the accompanying code for the book "SLAM in Autonomous Driving." It systematically introduces readers to core concepts such as inertial navigation, integrated navigation, LiDAR mapping, LiDAR localization, and LiDAR-inertial odometry. The repository allows users to reproduce classic algorithms and data structures in LiDAR SLAM, including Error-State Kalman Filters, pre-integration systems, 2D and 3D LiDAR mapping algorithms like ICP and NDT, and tightly-coupled LIO systems. The implementations are designed to be simpler than those found in comparable libraries, making it easier to understand their workings. It also supports concurrent programming for efficient execution and includes dynamic demonstrations for each chapter.

Best used for

Ideal for developers and data scientists who need to implement and understand SLAM algorithms for autonomous driving, reproduce classic LiDAR SLAM algorithms, and build offline mapping systems. Especially valuable for students and researchers looking for simplified, open-source implementations of complex navigation and mapping techniques.

Common actions

implement SLAM algorithms

develop autonomous driving

learn LiDAR mapping

reproduce classic algorithms

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Capabilities

Key features

Inertial navigation implementation
LiDAR mapping algorithms
LiDAR-inertial odometry
Error-State Kalman Filter
Pre-integration systems
ICP and NDT algorithms
Offline mapping system

Target Audience

developerdata scientiststudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of datasets are supported by slam_in_autonomous_driving?

The tool supports various large datasets for testing, including UrbanLoco, NCLT, WXB, 2dmapping, AVIA, and UTBM, totaling 270GB. It also uses built-in text-formatted IMU and RTK data for chapters 3-4, and EPFL data for point cloud registration in chapter 7.

What are the recommended system requirements for compiling the code?

The recommended environment is Ubuntu 20.04 with ROS Noetic. Users will also need to install additional libraries like PCL, OpenCV, glog, Eigen3, SuiteSparse, yaml-cpp, btbb, gmock, Pangolin, and g2o. Adaptations for Ubuntu 18.04 are also provided, including GCC-9 installation or Docker usage.

Does the repository include an English translation of the accompanying book?

Yes, an English translation of the book "SLAM in Autonomous Driving" is almost complete and is open-source. It can be accessed via a provided link within the repository's README, allowing a broader audience to benefit from the content.

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