A stochastic map for uncertain spatial relationships
Proceedings of the 4th international symposium on Robotics Research
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Field Robotics - Special Issue on the 2007 DARPA Urban Challenge, Part III
Simultaneous local and global state estimation for robotic navigation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Semantic structure from motion: a novel framework for joint object recognition and 3d reconstruction
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Moving object detection with laser scanners
Journal of Field Robotics
Challenging data sets for point cloud registration algorithms
International Journal of Robotics Research
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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.