GitHub repository
A real-time, tightly-coupled lidar-inertial odometry package for robust robot localization and mapping.
What is GitHub repository?
LIO-SAM is a real-time lidar-inertial odometry package that tightly couples lidar and IMU data to estimate a robot's position and orientation while simultaneously building a map of the environment. It solves the problem of accurate localization and mapping in dynamic or GPS-denied settings, such as indoor navigation or outdoor terrain traversal. The system uses factor graph optimization to fuse sensor inputs efficiently, enabling robust performance even with high-frequency sensor data.
Robotics researchers, engineers, and developers working on autonomous vehicles, drones, or mobile robots that require precise, real-time localization and mapping capabilities. It is particularly suited for those integrating lidar and IMU sensors in ROS-based systems.
Developers choose LIO-SAM for its tightly-coupled sensor fusion approach, which provides higher accuracy and robustness compared to loosely-coupled methods. Its real-time performance, support for multiple lidar types, and integration with GPS for global correction make it a versatile and reliable solution for challenging robotics applications.
Overview
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
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