Camera Pose Estimation of a Smartphone at a Field without Interest Points

Abstract

An Augmented Reality (AR) system on mobile phones has recently attracted attention because smartphones have increasingly been popular. For an AR system, we have to know a camera pose of a smartphone. A sensor-based method is one of the most popular ways to estimate the camera pose, but it cannot estimate an accurate pose. A vision-based method is another way to estimate the camera pose, but it is not suitable to a scene with few interest points such as a sports field. In this paper, we propose a novel method of a camera pose estimation for a scene without interest points by combining a sensor-based and a vision-based approach. In our proposed method, we use an acceleration and a magnetic sensor to roughly estimate a camera pose, then search the accurate pose by matching a captured image with a set of reference images. Our experiments show that our proposed method is accurate and fast enough to apply a real-time AR system.

A new method for automatic and accurate coded target recognition in oblique images to improve augmented reality precision

“…AR applications need to know where the user is and what he/she is looking at. As a result, in practice, the system needs to detect the position and orientation of the camera (Blum, Greencorn, & Cooperstock, 2013; Miyano, Inoue, Minagawa, Uematsu, & Saito, 2013). In most AR applications, the internal orientation parameters (IOP) of the camera are fixed and calibrated before the estimation process of the EOP begins.…”

Relative Pose Estimation Algorithm with Gyroscope Sensor

“…This is because gyroscope data need to be integrated only once to obtain the camera's orientation but accelerometer data need to be integrated twice to obtain the camera's translation, which will introduce too much noise that will affect significantly the accuracy. Miyano et al [8] proposed an inertial and visual combination solution. It uses acceleration and a magnetic sensor to roughly estimate a camera pose and then searches the accurate pose by matching a captured image with a set of reference images.…”

“…With the fast growing employment of MEMS sensors in smart devices, inertial-based solutions for CPE problem have been tried recently. These solutions [7,8] usually first perform CPE by visual and inertial methods individually and then adopt data filter to fuse the two results in order to obtain a more reliable estimation result. These two individual algorithms complement each other and improve the robustness of CPE.…”

A Mobile AR System for Sports Spectators using Multiple Viewpoint Cameras

“…To address each problem of the sensor-based and the vision-based approach, we proposed to combine both approaches (Miyano et al, 2012). In a visionbased approach, a transformed image created from a panorama image and sensor information is used to estimate a capturing position.…”