XR-VIO: High-precision Visual Inertial Odometry with Fast Initialization for XR Applications

TL;DR

This paper introduces XR-VIO, a high-precision visual inertial odometry system with a robust initialization pipeline and a hybrid feature matching method, achieving state-of-the-art accuracy and robustness for XR applications.

Contribution

The paper presents a novel initialization pipeline that robustly handles complex scenarios and a hybrid feature matching approach combining optical flow and descriptor matching.

Findings

Stable performance with only four image frames

State-of-the-art accuracy and success rate on benchmarks

Practical applicability demonstrated on mobile AR/VR devices

Abstract

This paper presents a novel approach to Visual Inertial Odometry (VIO), focusing on the initialization and feature matching modules. Existing methods for initialization often suffer from either poor stability in visual Structure from Motion (SfM) or fragility in solving a huge number of parameters simultaneously. To address these challenges, we propose a new pipeline for visual inertial initialization that robustly handles various complex scenarios. By tightly coupling gyroscope measurements, we enhance the robustness and accuracy of visual SfM. Our method demonstrates stable performance even with only four image frames, yielding competitive results. In terms of feature matching, we introduce a hybrid method that combines optical flow and descriptor-based matching. By leveraging the robustness of continuous optical flow tracking and the accuracy of descriptor matching, our approach achieves efficient, accurate, and robust tracking results. Through evaluation on multiple benchmarks, our method demonstrates state-of-the-art performance in terms of accuracy and success rate. Additionally, a video demonstration on mobile devices showcases the practical applicability of our approach in the field of Augmented Reality/Virtual Reality (AR/VR).