Answers are grounded in this paper and cite their sources.
This paper presents a large scale dataset of vision (stereo and RGB-D), laser and proprioceptive data collected over an extended duration by a Willow Garage PR2 robot in the 10 story MIT Stata Center. As of September 2012 the dataset comprises over 2.3 TB, 38 h and 42 km (the length of a marathon). The dataset is of particular interest to robotics and computer vision researchers interested in long-term autonomy. It is expected to be useful in a variety of research areas—robotic mapping (long-term, visual, RGB-D or laser), change detection in indoor environments, human pattern analysis, long-term path planning. For ease of use the original ROS ‘bag’ log files are provided and also a derivative version combining human readable data and imagery in standard formats. Of particular importance, this dataset also includes ground-truth position estimates of the robot at every instance (to typical accuracy of 2 cm) using as-built floor-plans—which were carefully extracted using our software tools. The provision of ground-truth for such a large dataset enables more meaningful comparison between algorithms than has previously been possible.
No takes yet. Share an insight, caveat, or question.
Fallon et al. (2013) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: