Definition of an Enhanced Map-Matching Algorithm for Urban Environments with Poor GNSS Signal Quality

نویسندگان

  • Felipe Jiménez
  • Sergio Monzón
  • José Eugenio Naranjo
چکیده

Vehicle positioning is a key factor for numerous information and assistance applications that are included in vehicles and for which satellite positioning is mainly used. However, this positioning process can result in errors and lead to measurement uncertainties. These errors come mainly from two sources: errors and simplifications of digital maps and errors in locating the vehicle. From that inaccurate data, the task of assigning the vehicle's location to a link on the digital map at every instant is carried out by map-matching algorithms. These algorithms have been developed to fulfil that need and attempt to amend these errors to offer the user a suitable positioning. In this research; an algorithm is developed that attempts to solve the errors in positioning when the Global Navigation Satellite System (GNSS) signal reception is frequently lost. The algorithm has been tested with satisfactory results in a complex urban environment of narrow streets and tall buildings where errors and signal reception losses of the GPS receiver are frequent.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

High-Accuracy Positioning in Urban Environments Using Single-Frequency Multi-GNSS RTK/MEMS-IMU Integration

The integration of Global Positioning System (GPS) real-time kinematics (RTK) and an inertial navigation system (INS) has been widely used in many applications, such as mobile mapping and autonomous vehicle control. Such applications require high-accuracy position information. However, continuous and reliable high-accuracy positioning is still challenging for GPS/INS integration in urban enviro...

متن کامل

Enhancing GNSS Positioning with 3D Mapping

The number of global navigation satellite systems (GNSS) signals that can be received in dense urban areas has increased through the availability of multiple satellite constellations and high sensitivity receivers. However, in these constrained environments, the blockage and reflection of many of the signals by buildings and other obstacles means that poor positioning accuracy remains a problem...

متن کامل

Collaborative Opportunistic Navigation

Despite the extraordinary advances in global navigation satellite systems (GNSS), the inherent limitation of the weakness of their space-based signals makes such signals easy to block intentionally or accidentally. This makes GNSS insufficient for reliable anytime, anywhere navigation, particularly in GNSS-challenged environments, such as indoors, deep urban canyons, and GNSS-denied environment...

متن کامل

Provide a Deep Convolutional Neural Network Optimized with Morphological Filters to Map Trees in Urban Environments Using Aerial Imagery

Today, we cannot ignore the role of trees in the quality of human life, so that the earth is inconceivable for humans without the presence of trees. In addition to their natural role, urban trees are also very important in terms of visual beauty. Aerial imagery using unmanned platforms with very high spatial resolution is available today. Convolutional neural networks based deep learning method...

متن کامل

A New Approach for Improving Reliability of Personal Navigation Devices under Harsh GNSS Signal Conditions

In natural and urban canyon environments, Global Navigation Satellite System (GNSS) signals suffer from various challenges such as signal multipath, limited or lack of signal availability and poor geometry. Inertial sensors are often employed to improve the solution continuity under poor GNSS signal quality and availability conditions. Various fault detection schemes have been proposed in the l...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Sensors

دوره 16 2  شماره 

صفحات  -

تاریخ انتشار 2016