koide3/glim

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1758 stars · 272 forks observed · 2026-08-28

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

GLIM is a versatile and extensible point cloud-based 3D localization and mapping (SLAM) framework written in C++. It performs direct multi-scan registration on factor graphs with optional GPU acceleration and supports a wide range of range sensors including spinning, non-repetitive, and solid-state LiDARs and RGB-D cameras.

Use cases

• build 3D maps from spinning LiDAR data like Velodyne or Ouster
• run LiDAR-inertial odometry and mapping with Livox MID360
• map indoor environments with an RGB-D camera or RealSense L515
• correct mapping failures interactively and refine 3D maps
• extend SLAM with custom loop closure or LiDAR-visual-inertial constraints
• run real-time 3D localization on NVIDIA Jetson for a robot
• merge multiple mapping sessions into a single consistent map

When to choose

• you need accurate factor-graph-based 3D LiDAR or RGB-D SLAM with GPU acceleration
• you want a sensor-agnostic mapping framework that works with many range sensor types
• you need interactive map correction and extensibility via callback slots and extension modules
• you are deploying SLAM on Ubuntu with CUDA or NVIDIA Jetson hardware

When to avoid

• you need visual-only or 2D SLAM rather than range-based 3D mapping
• you require a pure CPU pipeline without CUDA dependencies and want GPU-free simplicity
• you need a turnkey commercial SLAM product rather than a research-oriented C++ framework
• your platform is Windows or macOS without Linux/CUDA support

Facets

framework · maturity active

computer-vision graphics simulation gpu-computing middleware robotics autonomous-vehicles computer-vision developer-tools cpp slam lidar point-cloud mapping localization factor-graph imu ros2 3d-mapping odometry linux ros gpu docker

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