Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Introduction to Autonomous Mobile Robots
Introduction to Autonomous Mobile Robots
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
Odin: Team VictorTango's entry in the DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Team Cornell's Skynet: Robust perception and planning in an urban environment
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Autonomous driving in urban environments: Boss and the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part I
Junior: The Stanford entry in the Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
Little Ben: The Ben Franklin Racing Team's entry in the 2007 DARPA Urban Challenge
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part II
A perception-driven autonomous urban vehicle
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Autonomous robot navigation in outdoor cluttered pedestrian walkways
Journal of Field Robotics
3-D mapping of natural environments with trees by means of mobile perception
IEEE Transactions on Robotics
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This paper presents an approach for vehicle 3D localization in outdoor woodland environments using a loosely coupled multisensor system. The vehicle 3D dead reckoning is computed using a wheel encoder and an IMU. Dead reckoning is corrected from three different sources: a)Using a tilted lidar for road detection and computation of the vehicle position within the road which is then corrected towards a 2D road centerline map given in advance. b) DGPS 2D or 3D data as available. c) Under tree foliage DGPS blackouts commonly occur, specially when measuring height, therefore the use of a barometer for correcting height is proposed. An extended Kalman filter is used for sensor fusion and pose estimation. Finally, the estimated vehicle height is added to the 2D map obtaining a 3D road centerline map with width (measured by the tilted lidar). Thoroughly experimentation on real mountainous woodland paths show the usefulness and robustness of the proposed approach.