Two years of Visual Odometry on the Mars Exploration Rovers: Field Reports
Journal of Field Robotics - Special Issue on Space Robotics
The New College Vision and Laser Data Set
International Journal of Robotics Research
Fast registration based on noisy planes with unknown correspondences for 3-D mapping
IEEE Transactions on Robotics
The Marulan Data Sets: Multi-sensor Perception in a Natural Environment with Challenging Conditions
International Journal of Robotics Research
A High-rate, Heterogeneous Data Set From The DARPA Urban Challenge
International Journal of Robotics Research
Ford Campus vision and lidar data set
International Journal of Robotics Research
Comparing ICP variants on real-world data sets
Autonomous Robots
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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The number of registration solutions in the literature has bloomed recently. The iterative closest point, for example, could be considered as the backbone of many laser-based localization and mapping systems. Although they are widely used, it is a common challenge to compare registration solutions on a fair base. The main limitation is to overcome the lack of accurate ground truth in current data sets, which usually cover environments only over a small range of organization levels. In computer vision, the Stanford 3D Scanning Repository pushed forward point cloud registration algorithms and object modeling fields by providing high-quality scanned objects with precise localization. We aim to provide similar high-caliber working material to the robotic and computer vision communities but with sceneries instead of objects. We propose eight point cloud sequences acquired in locations covering the environment diversity that modern robots are susceptible to encounter, ranging from inside an apartment to a woodland area. The core of the data sets consists of 3D laser point clouds for which supporting data (Gravity, Magnetic North and GPS) are given for each pose. A special effort has been made to ensure global positioning of the scanner within mm-range precision, independent of environmental conditions. This will allow for the development of improved registration algorithms when mapping challenging environments, such as those found in real-world situations.1