Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Abstract

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.

Semantic VPS for Smartphone Localization in Challenging Urban Environments

“…The experimental ground truth positions were determined based on Google Earth and nearby identifiable landmarks, such as a labelled corner on the ground. Based on the experience of previous research [ 18 , 32 ], the ground truth uncertainty of latitude and longitude was and yaw was . The pitch and roll angles were measured using the XPRO geared head, Manfrotto , with uncertainty.…”

“…The positioning quality of the proposed method was analyzed based on the ideal manual segmentation of the smartphone image. The experimental results were then postprocessed and compared to the ground truth and different positioning algorithms as shown in Table 3 [18,32], the ground truth uncertainty of latitude and longitude was ±1 and yaw was ±2°.…”

“…The pitch and roll angles were measured using the XPRO geared head, Manfrotto, with ±1° uncertainty. [18,32], the ground truth uncertainty of latitude and longitude was ±1 and yaw was ±2°.…”

“…Research has demonstrated that it is possible to obtain precise positioning by matching between a camera image and a database of images. One popular approach uses sky-pointing fisheye camera equipment to detect obstacles and buildings in the local environment [ 18 ]. When used in conjunction with image processing algorithms, this approach allows the matching of the building boundary skyplot (skymask) to obtain a position and heading.…”

A Feasibility Study on Smartphone Localization using Image Registration with Segmented 3D Building Models based on Multi-Material Classes

“…In the context of outdoor pedestrian localization, the application of global navigation satellite system (GNSS) is the key technology to provide accurate positioning/timing service in open field environments. Unfortunately, its positioning performance in urban areas still has a lot of potential to improve due to signal blockages and reflections caused by tall buildings and dense foliage [3,4].…”