Visual Indoor Positioning with a Single Camera Using PnP

This paper introduces an accurate and inexpensive method for localizing a calibrated monocular camera in 3D indoor environments. The objective of this work is to localize in 6 degrees-of-freedom (6 DOF) in the presence of a 3D map that contains 3D point clouds co-registered with intensity information. This is done by solving the Perspective-n-Point (PnP) problem to accurately compute the camera location in 6 DOF. An efficient data structure is used to store a large set of point clouds co-registered with intensity information, image features, and transformations between the images. This data structure, referred to as the feature database, is implemented such that it retrieves a match for a query image efficiently. Thus the overall process of localization in 6 DOF becomes a real-time process with high efficiency and accuracy. Our technique was tested with two ground truth data sets of indoor environments, an office and a laboratory. The experimental results show the accuracy and the efficiency of our technique, with an average localization error of less than 10 mm from the ground truth in both environments. In addition, localization results on query images obtained using two different cameras in four different environments are presented. This demonstrates that any type of monocular camera may be used during localization, as long as a sufficient number of environmental features can be extracted from the query images.