An open-source GNSS/INS simulation tool that generates sensor data, runs navigation algorithms, and visualizes results for inertial navigation systems.
GNSS-INS-SIM is an open-source simulation tool for GNSS and inertial navigation systems. It generates reference trajectories, sensor outputs (IMU, GPS, magnetometer, odometer), and allows users to test and visualize navigation algorithms in a controlled environment. The tool solves the problem of needing physical hardware for algorithm development and validation in robotics, aerospace, and autonomous vehicle applications.
Navigation engineers, robotics researchers, and aerospace developers working on sensor fusion, inertial navigation, and GNSS/INS algorithm design and testing.
Developers choose GNSS-INS-SIM for its comprehensive feature set, flexibility in sensor modeling and algorithm integration, and open-source nature, which eliminates the need for expensive proprietary simulation software and provides full control over the simulation environment.
Open-source GNSS + inertial navigation, sensor fusion simulator. Motion trajectory generator, sensor models, and navigation
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Supports custom IMU error definitions and built-in accuracy levels ('low-accuracy' to 'high-accuracy'), allowing for realistic sensor data generation tailored to specific hardware.
Enables running multiple custom navigation algorithms in a single simulation for direct comparison, as shown in demo_multiple_algorithms.py, facilitating robust validation.
Includes vibration models (random, sinusoidal, PSD) that can be applied to IMU data, adding environmental realism to simulations without physical hardware.
Uses a standardized Python class structure for user-defined algorithms, making it easy to plug in and test custom navigation filters or fusion techniques.
Defining motion profiles requires creating CSV files with specific command types and parameters, which can be tedious and error-prone for complex trajectories.
Assumes familiarity with navigation concepts like reference frames (NED vs. virtual inertial) and sensor error models, limiting accessibility for newcomers without prior domain knowledge.
As a niche tool, it lacks a broad community or extensive third-party integrations, making it harder to find pre-built algorithms or troubleshooting help compared to mainstream platforms.