XIVO: Premier Open Source Tool for Visual-Inertial Estimation
XIVO, abbreviated from X Inertial-Visual Odometry, is a public GitHub project developed by the UCLA's Vision Lab, serving the purpose of implementing visual-inertial estimation. Its significance cannot be overstated as XIVO provides an open-source solution for state-of-the-art and real-time monocular vision and inertial navigation.
Project Overview:
The XIVO project, at its core, aims to offer a comprehensive implementation of monocular vision and inertial sensor fusion. It addresses the need for an integrated tool for visual-inertial estimation in real-time applications such as robotic navigation, virtual and augmented reality. The primary users of this project are developers, researchers, and various tech enthusiasts, particularly those working in the field of AI, computer vision, and robotics.
Project Features:
Some key features of XIVO are real-time monocular visual SLAM (Simultaneous Localization and Mapping), sensor fusion with inertial measurement units (IMUs), handling of fast camera motions and long exposure times, handling of feature-rich environments, scale-drift correction, and global shutter model support.
Through these features, XIVO significantly enhances the quality of visual-inertial sensor fusion while catering to real-time requirements. For instance, it's capable of tackling fast camera motions, a common challenge in robotics and AR/VR applications that involve quick movements.
Technology Stack:
XIVO's underlying technologies include C++ for the major part of the project, and Python is used for some scripts such as ground truth data simulations. Eigen library, built using C++, is extensively utilized for linear algebra operations, while g2o, another C++ based framework, is used for robust graph-based optimization.
These technologies were chosen largely due to their efficiency and robustness, which are essential to meet XIVO's goal of delivering a real-time functioning tool.
Project Structure and Architecture:
The XIVO project maintains a well-structured and easily comprehensible architecture. It is divided into several modules, including Feature, Tracker, Estimator, GTSAM, and others. Each module plays a specific role within the system; for example, the 'Feature' module is responsible for feature detection and tracking, while the 'Estimator' module performs state estimation.
The interplay of these components is managed by the Tracker-Estimator-Viewer design pattern, ensuring efficient processing and real-time performance.
