The International Journal of Robotics Research · 2011 · 352 citations · 8 references
ComputingAutonomous SystemsRobotics EngineeringAutonomous VehiclesComputer VisionRobot VisionMappingPoint Cloud ProcessingRobot LocalizationSimultaneous LocalizationVehicle PerceptionVisual SlamLidar Data CollectionLidar LocalizationLidar SensingLidar-based LocalizationLocalization Using LidarMapping AlgorithmsVehicle Path FollowingVehicle Trajectory Analysis
In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November–December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.
8
A stochastic map for uncertain spatial relationships
Randall K. Smith, Matthew W. Self, Peter Cheeseman · 1988 · 421 citations
LCM: Lightweight Communications and Marshalling
Alice Huang, Edwin Olson, David C. Moore · 2010 · 301 citations
Extrinsic Calibration of a 3D Laser Scanner and an Omnidirectional Camera
Gaurav Pandey, James R. McBride, Silvio Savarese et al. · IFAC Proceedings Volumes · 2010 · 144 citations
Engineering, Physics, Machine Vision +14