Title
Monocular visual navigation of an autonomous vehicle in natural scene corridor-like environments
Abstract
We present a monocular visual navigation methodology for autonomous orchard vehicles. Modern orchards are usually planted with straight and parallel tree rows that form a corridor-like environment. Our task consists of driving a vehicle autonomously along the tree rows. The original contributions of this paper are: 1) a method to recover vehicle rotation independently of translation by modeling the vehicle as a car-like robot driving on a 3D ground surface-the rotation is estimated from monocular images while the translation is measured by a wheel encoder; and 2) a method to fit the 3D points corresponding to the trees into straight lines via an optimization algorithm that minimizes the error variance on the robot lookahead point. Additionally, we use a simple vanishing point detection approach to find the ends of the tree rows. The vanishing point detection is integrated into the system via an extended Kalman filter. The methodology's robustness to environmental changes is validated in more than fifty experiments in research and commercial orchards, six of which are presented and discussed in detail.
Year
DOI
Venue
2012
10.1109/IROS.2012.6385479
IROS
Keywords
Field
DocType
optimisation,kalman filters,robot lookahead point,vanishing point detection approach,monocular visual navigation,3d ground surface,natural scene corridor-like environments,mobile robots,autonomous orchard vehicles,corridor-like environment,optimization algorithm,extended kalman filter,robot vision,vegetation,image reconstruction,robot kinematics
Computer vision,Extended Kalman filter,Computer science,Robot kinematics,Kalman filter,Artificial intelligence,Mobile robot navigation,Monocular,Robot,Vanishing point,Mobile robot
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4673-1737-5
1
PageRank 
References 
Authors
0.42
22
4
Name
Order
Citations
PageRank
Ji Zhang1335.56
George Kantor281.87
Marcel Bergerman322534.88
Sanjiv Singh42388217.44