A review of Visual Inertial Odometry for object tracking and measurement

• Main Authors: Ismail, Nor Azman, Tan, Chun Wen, Salam, Md. Sah, Mohd. Nawi, Abdullah, Mohamed, Su Elya Namira
• Format: Article
• Published: International Journal of Scientific and Technology Research 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/90905/; https://www.ijstr.org/research-paper-publishing.php?month=feb2020

This paper aims to explore the use of Visual Inertial Odometry (VIO) for tracking and measurement. The evolution of VIO is first discussed, followed by the overview of monocular Visual Odometry (VO) and the Inertial Measurement Unit (IMU). Next, the related measurement approaches and the use of VIO for measurement have also been discussed. Visual Inertial Odometry is the combination of IMU in the VO system in which the visual information and inertial measurements are combined to achieve an accurate measurement. The algorithm of VO system contains four components, which are camera calibration algorithm, the feature tracker algorithm (usually the KLT algorithm), the rigid motion estimation algorithm, and the algorithm that matches a description of the features points (typically RANSAC algorithm). The IMU is the combination of accelerometer, gyroscopes and magnetometer that measures the linear and angular motion. To fuse the visual and inertial measurements data, there are two different approaches based on when and how they were fused. Tightly coupled and loosely coupled are the approaches for when the measurements are fused, while filtering and optimization based are the approaches for how they were fused. Studies on related measurement approaches can be summarized as three methods which are using the time-of-flight camera, dual cameras (stereovision), or the single camera known as monovision. This review shows that the technique that utilizes the VIO to get visual information and inertial motion has been used widely for measurement lately especially for the field related to Augmented Reality.