A plane tracker for AEC-automation applications

Abstract

Purpose We propose a new registration algorithm and computing framework, the keg tracker, for estimating a camera's position and orientation for a general class of mobile context-aware applications in architecture, engineering, and construction (AEC). Method By studying two classic types of natural marker-based registration algorithms, homographyfrom-detection and homography-from-tracking, and overcoming their specific limitations of jitter and drift, our method applies two global constraints (geometric and appearance) to prevent tracking errors from propagating between consecutive frames. Results & Discussion The proposed method is able to achieve an increase in both stability and accuracy, while being fast enough for real-time applications. Experiments on both synthesized and real world test cases demonstrate that our method is superior to existing state-of-the-art registration algorithms. The paper also explores several AEC-applications of our method in context-aware computing and desktop augmented reality.