This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva's software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. During 2 weeks of operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44 km at speeds of up to 163 cm/sec in the unmodified museum.
Abstract-In this paper we present a method for fast surface reconstruction from large noisy datasets. Given an unorganized 3D point cloud, our algorithm recreates the underlying surface's geometrical properties using data resampling and a robust triangulation algorithm in near realtime. For resulting smooth surfaces, the data is resampled with variable densities according to previously estimated surface curvatures. Incremental scans are easily incorporated into an existing surface mesh, by determining the respective overlapping area and reconstructing only the updated part of the surface mesh. The proposed framework is flexible enough to be integrated with additional point label information, where groups of points sharing the same label are clustered together and can be reconstructed separately, thus allowing fast updates via triangular mesh decoupling. To validate our approach, we present results obtained from laser scans acquired in both indoor and outdoor environments.
We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, fullbody motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations.
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