Robust Monte Carlo localization for mobile robots
Artificial Intelligence
Navigating Mobile Robots: Systems and Techniques
Navigating Mobile Robots: Systems and Techniques
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
Real-time implementation of a GIS-based localization system for intelligent vehicles
EURASIP Journal on Embedded Systems
Application of transferable belief model to navigation system
Integrated Computer-Aided Engineering - Informatics in Control, Automation and Robotics
Vehicular Ad Hoc Networks: A New Challenge for Localization-Based Systems
Computer Communications
Seeing our signals: combining location traces and web-based models for personal discovery
Proceedings of the 9th workshop on Mobile computing systems and applications
In-car positioning and navigation technologies: a survey
IEEE Transactions on Intelligent Transportation Systems
An approach to urban traffic state estimation by fusing multisource information
IEEE Transactions on Intelligent Transportation Systems
Terrain-based road vehicle localization on multi-lane highways
ACC'09 Proceedings of the 2009 conference on American Control Conference
Vehicle 3D localization in mountainous woodland environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Object association with belief functions, an application with vehicles
Information Sciences: an International Journal
Virtual 3D City Model for Navigation in Urban Areas
Journal of Intelligent and Robotic Systems
Vehicle localization using omnidirectional camera with GPS supporting in wide urban area
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume Part I
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This paper describes a novel road-matching method designed to support the real-time navigational function of cars for advanced systems applications in the area of driving assistance. This method provides an accurate estimation of position for a vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and ABS sensor measurements to produce an approximation of the vehicle's pose, which is then used to select the most likely segment from the database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory. A new observation is then built using the selected segment, and the approximate pose adjusted in a second Kalman filter estimation stage. The particular attention given to the modeling of the system showed that incrementing the state by the bias (also called absolute error) of the map significantly increases the performance of the method. Real experimental results show that this approach, if correctly initialized, is able to work over a substantial period without GPS.