Enhancement of GPS Position Accuracy Using Machine Vision and Deep Learning Techniques

Ashwani Kumar Aggarwal

doi:10.3844/jcssp.2020.651.659

Abstract

The accuracy of GPS position estimation in urban cities is an issue which need to be resolved using machine vision and deep learning techniques. The accuracy of GPS in horizontal direction is better than in the vertical direction. Although for most of the navigation applications in intelligent transportation systems, horizontal positioning accuracy is vital, but vertical position accuracy gives idea about road slanting conditions. Several statistical methods like median filtering, homomorphic filtering and k-means clustering, etc., can be used to improve upon the position accuracy of GPS signals. Such methods are useful for offline applications where a lot many GPS measurements are taken at a single point and afterwards filtering is applied to batch of measurement. In this study, the GPS positioning errors which are caused by sensor noise, ionospheric effects, occlusions by building facades, etc., have been considered for online improvement in position estimation using computer vision and deep learning methods by empirically choosing hyper-parameters.

Highlights

GPS is a very common positioning method for estimation of position of a vehicle in the outdoor environment
The position of the GPS receiver obtained with respect to centre of earth is converted into more useful coordinate system
The GPS position estimation finds its applications in diverse domains viz. autonomous vehicles, mobile robots, augmented reality and gaming industry etc

Summary

Introduction

GPS is a very common positioning method for estimation of position of a vehicle in the outdoor environment. The accuracy of horizontal and vertical positioning of the GPS receiver depends on the signals coming of number of satellites orbiting the earth. The clock used in GPS receivers are not accurate enough which leads to errors in position accuracy because the position is calculated based on time of travel of radio waves from the satellites to the GPS receivers. The computer vision-based methods make use of images captured in the vicinity of the environment where the GPS receiver is sued to estimate its position. The deep learning methods work by collection of huge amounts of image data to train the network for its use in improvement of GPS accuracy. The convolutional neural network-based architecture of deep learning is quite useful to enhance the position accuracy of GPS estimates. The convolution operator is used at various layers so that features with low complexity like straight lines to higher complexity like triangles, hexagons could be learned during the training phase of deep neural network

Related Work

Conclusion