LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

A robust and accurate camera pose determination method based on geometric optimization search using Internet of Things

Photo by jadeaucamp from unsplash

We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information… Click to show full abstract

We propose a robust and accurate camera pose determination method based on geometric optimization search using the Internet of Things (IoT). The central idea is to (1) obtain image information through Internet of Things technology, (2) obtain the first pose by minimizing the error function, and (3) use the geometric relationship and constraint condition to obtain the appropriate attitude angles as a new initial value for the next iteration calculation. The features of this method are as follows. First, this method can deal with a large amount of uncertain data, such as in the case of any shooting angle, in the case of any reference point, and in the case of a small number of feature points. Finally, because of using Internet of Things technology, our method can quickly complete data processing and transmission. Compared to state-of-the-art methods, the experimental results show that our approach performs well on both synthetic and real data and can be used to provide accurate and stable data for subsequent applications.

Keywords: camera pose; method; internet things; accurate camera; robust accurate; using internet

Journal Title: International Journal of Distributed Sensor Networks
Year Published: 2019

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.