Abstract
This paper presents the Robot-at-Home dataset (Robot@Home), a collection of raw and processed sensory data from domestic settings aimed at serving as a benchmark for semantic mapping algorithms through the categorization of objects and/or rooms. The dataset contains 87,000+ time-stamped observations gathered by a mobile robot endowed with a rig of four RGB-D cameras and a 2D laser scanner. Raw observations have been processed to produce different outcomes also distributed with the dataset, including 3D reconstructions and 2D geometric maps of the inspected rooms, both annotated with the ground truth categories of the surveyed rooms and objects. The proposed dataset is particularly suited as a testbed for object and/or room categorization systems, but it can be also exploited for a variety of tasks, including robot localization, 3D map building, SLAM, and object segmentation. Robot@Home is publicly available for the research community at http://mapir.isa.uma.es/work/robot-at-home-dataset .
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