The document discusses a real-time computer vision and DGPS-aided inertial navigation system for lane-level vehicle navigation, addressing the limitations of traditional GPS in urban environments. It describes a sensor fusion technique that utilizes computer vision and landmark data to enhance positioning accuracy in challenging areas. Experimental results validate the proposed method, highlighting its efficacy in utilizing existing infrastructure like traffic lights for improved navigation.

1. Impulse Technologies
Beacons U to World of technology
044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
Real-Time Computer Vision/DGPS-Aided Inertial Navigation
System for Lane-Level Vehicle Navigation
Abstract
Many intelligent transportation system (ITS) applications will increasingly
rely on lane-level vehicle positioning that requires high accuracy, bandwidth,
availability, and integrity. Lane-level positioning methods must reliably work in
real time in a wide range of environments, spanning rural to urban areas.
Traditional positioning sensors such as the Global Navigation Satellite Systems
may have poor performance in dense urban areas, where obstacles block satellite
signals. This paper presents a sensor fusion technique that uses computer vision
and differential pseudorange Global Positioning System (DGPS) measurements to
aid an inertial navigation system (INS) in challenging environments where GPS
signals are limited and/or unreliable. To supplement limited DGPS measurements,
this method uses mapped landmarks that were measured through a priori
observations (e.g., traffic light location data), taking advantage of existing
infrastructure that is abundant within suburban/urban environments. For example,
traffic lights are easily detected by color vision sensors in both day and night
conditions. A tightly coupled estimation process is employed to use observables
from satellite signals and known feature observables from a camera to correct an
INS that is formulated as an extended Kalman filter. A traffic light detection
method is also outlined, where the projected feature uncertainty ellipse is utilized
to perform data association between a predicted feature and a set of detected
features. Real-time experimental results from real-world settings are presented to
validate the proposed localization method.
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