Paper Info
Title
CARLOC: Precise Positioning of Automobiles
Abstract
Precise positioning of an automobile to within lane-level precision can enable better navigation and context-awareness. However, GPS by itself cannot provide such precision in obstructed urban environments. In this paper, we present a system called CARLOC for lane-level positioning of automobiles. CARLOC uses three key ideas in concert to improve positioning accuracy: it uses digital maps to match the vehicle to known road segments; it uses vehicular sensors to obtain odometry and bearing information; and it uses crowd-sourced location of estimates of roadway landmarks that can be detected by sensors available in modern vehicles. CARLOC unifies these ideas in a probabilistic position estimation framework, widely used in robotics, called the sequential Monte Carlo method. Through extensive experiments on a real vehicle, we show that CARLOC achieves sub-meter positioning accuracy in an obstructed urban setting, an order-of-magnitude improvement over a high-end GPS device.
Year
DOI
Venue
2015
10.1145/2809695.2809725
Conference On Embedded Networked Sensor Systems
Keywords
Field
DocType
GPS,Map,Accuracy
Hybrid positioning system,Computer vision,Computer science,Digital mapping,Odometry,Bearing (mechanical),Real-time computing,Global Positioning System,Artificial intelligence,Probabilistic logic,Precise Point Positioning,Robotics
Conference
Citations 
PageRank 
References 
7
0.66
22
Authors
8
Name
Order
Citations
PageRank
Yurong Jiang112611.36
Hang Qiu2505.16
Matthew McCartney3111.47
Gaurav S. Sukhatme45469548.13
Marco Gruteser54631309.81
Fan Bai62017135.11
Donald Grimm7222.18
ramesh govindan8154302144.86