N-LOS GNSS signal detection using fish-eye camera for vehicle navigation in urban environments

This paper describes a precision positioning technique that can be applied to vehicles in urban environments. There are numerous situations in urban environments in which an antenna can receive non-line-of-sight (N-LOS) signals, such as the reflection or diffraction signals from invisible satellites obstructed by buildings. In this situation, the multipath signals from the invisible satellites cause a large positioning error, and it is difficult to mitigate the multipath error in the global navigation satellite system positioning. In this study, we propose a novel technique to detect N-LOS satellites using only consumer-ready visible-light fish-eye cameras. The challenge with this technique is that it is difficult to robustly detect the borderline between the sky and the obstacles from the colored fish-eye image because detection using the color camera is affected by cloud cover, illumination conditions, and weather conditions. In this study, we use superpixel segmentation to classify the fish-eye image into the sky area and obstacles. Further, a sample of the most probable visible satellite extraction is developed to robustly detect the N-LOS satellites to estimate the location of a vehicle in urban environments. We also propose a technique to estimate the camera orientation rate from sequential fish-eye images, which is required for projecting the position of the satellite onto the fish-eye image. Using our proposed technique, kinematic evaluations were carried out in which invisible satellites were excluded via observations obtained using a fish-eye camera. The N-LOS satellites, which contain a large multipath error, can be correctly detected and excluded from the position calculation. The evaluation results confirm the effectiveness of the proposed technique and the feasibility of its highly accurate positioning in urban environments.