GICI-LIB: A GNSS/INS/Camera Integrated Navigation Library

TL;DR

This paper introduces GICI-LIB, an open-source library for GNSS/INS/camera integrated navigation, featuring a flexible factor graph framework that enhances multi-sensor fusion accuracy and robustness in diverse environments.

Contribution

The paper presents a comprehensive, flexible, and open-source GNSS/INS/camera integration library with a detailed dataset and a factor graph optimization framework for improved navigation accuracy.

Findings

GICI-LIB effectively combines multiple GNSS error sources for robust navigation.

The framework supports various algorithms like RTK and PPP with high accuracy.

Results demonstrate continuous precise navigation in urban environments.

Abstract

Accurate navigation is essential for autonomous robots and vehicles. In recent years, the integration of the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and camera has garnered considerable attention due to its robustness and high accuracy in diverse environments. However, leveraging the full capacity of GNSS is cumbersome because of the diverse choices of formulations, error models, satellite constellations, signal frequencies, and service types, which lead to different precision, robustness, and usage dependencies. To clarify the capacity of GNSS algorithms and accelerate the development efficiency of employing GNSS in multi-sensor fusion algorithms, we open source the GNSS/INS/Camera Integration Library (GICI-LIB), together with detailed documentation and a comprehensive land vehicle dataset. A factor graph optimization-based multi-sensor fusion framework is established, which combines almost all GNSS measurement error sources by fully considering temporal and spatial correlations between measurements. The graph structure is designed for flexibility, making it easy to form any kind of integration algorithm. For illustration, Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and four RTK-based algorithms from GICI-LIB are evaluated using our dataset and public datasets. Results confirm the potential of the GICI system to provide continuous precise navigation solutions in a wide spectrum of urban environments.