rpng/MINS
An efficient and robust multisensor-aided inertial navigation system with online calibration that is capable of fusing IMU, camera, LiDAR, GPS/GNSS, and wheel sensors. Use cases: VINS/VIO, GPS-INS, LINS/LIO, multi-sensor fusion for localization and mapping (SLAM). This repository also provides multi-sensor simulation and data.
What it solves
MINS addresses the difficulty of fusing data from multiple asynchronous sensors—such as cameras, LiDAR, GNSS, wheel encoders, and IMUs—into a single, robust navigation system. It overcomes common challenges like high computational complexity and the need for precise intra-sensor calibration to provide accurate localization for robots.
How it works
It uses a filtering-based approach to fuse five different sensing modalities. The system employs a dynamic cloning strategy and high-order state manifold interpolation to maintain lightweight estimation performance while ensuring consistency. It also performs online calibration of all onboard sensors to ensure they work together accurately in real-time.
Who it’s for
This project is designed for robotics researchers and engineers developing autonomous systems that require high-precision localization across various sensor combinations (e.g., VINS, LiDAR-IMU, or full multi-sensor suites).
Highlights
- Flexible Sensor Fusion: Supports arbitrary combinations of IMU, wheel encoders, cameras, LiDARs, and GNSS.
- Online Calibration: Automatically calibrates onboard sensors during operation.
- Lightweight Performance: Uses dynamic cloning and manifold interpolation to reduce computational load.
- Comprehensive Tooling: Includes a multi-sensor simulation toolbox and an evaluation suite for analyzing accuracy, timing, and consistency.
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