  Navigation inside a closed area with no GPS-signal accessibility is a highly
challenging task. In order to tackle this problem, recently the imaging-based
methods have grabbed the attention of many researchers. These methods either
extract the features (e.g. using SIFT, or SOSNet) and map the descriptive ones
to the camera position and rotation information, or deploy an end-to-end system
that directly estimates this information out of RGB images, similar to PoseNet.
While the former methods suffer from heavy computational burden during the test
process, the latter suffers from lack of accuracy and robustness against
environmental changes and object movements. However, end-to-end systems are
quite fast during the test and inference and are pretty qualified for
real-world applications, even though their training phase could be longer than
the former ones. In this paper, a novel multi-modal end-to-end system for
large-scale indoor positioning has been proposed, namely APS (Alpha Positioning
System), which integrates a Pix2Pix GAN network to reconstruct the point cloud
pair of the input query image, with a deep CNN network in order to robustly
estimate the position and rotation information of the camera. For this
integration, the existing datasets have the shortcoming of paired RGB/point
cloud images for indoor environments. Therefore, we created a new dataset to
handle this situation. By implementing the proposed APS system, we could
achieve a highly accurate camera positioning with a precision level of less
than a centimeter.
