A Flexible-Frame-Rate Vision-Aided Inertial Object Tracking System for Mobile Devices

TL;DR

This paper introduces a real-time, flexible-frame-rate object tracking system for mobile devices that combines visual and inertial data, ensuring high accuracy and robustness at up to 120 FPS.

Contribution

It presents a novel client-server architecture with inertial pose propagation and RGB-based pose estimation, including a bias correction and failure detection mechanism.

Findings

Supports up to 120 FPS tracking

Achieves high accuracy and robustness

Operates efficiently on low-end devices

Abstract

Real-time object pose estimation and tracking is challenging but essential for emerging augmented reality (AR) applications. In general, state-of-the-art methods address this problem using deep neural networks which indeed yield satisfactory results. Nevertheless, the high computational cost of these methods makes them unsuitable for mobile devices where real-world applications usually take place. In addition, head-mounted displays such as AR glasses require at least 90~FPS to avoid motion sickness, which further complicates the problem. We propose a flexible-frame-rate object pose estimation and tracking system for mobile devices. It is a monocular visual-inertial-based system with a client-server architecture. Inertial measurement unit (IMU) pose propagation is performed on the client side for high speed tracking, and RGB image-based 3D pose estimation is performed on the server side to obtain accurate poses, after which the pose is sent to the client side for visual-inertial fusion, where we propose a bias self-correction mechanism to reduce drift. We also propose a pose inspection algorithm to detect tracking failures and incorrect pose estimation. Connected by high-speed networking, our system supports flexible frame rates up to 120 FPS and guarantees high precision and real-time tracking on low-end devices. Both simulations and real world experiments show that our method achieves accurate and robust object tracking.