Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva

Thrun, Sebastian; Beetz, Michael; Bennewitz, Maren; Burgard, Wolfram; Cremers, Armin B.; Dellaert, Frank; Fox, Dieter; Hähnel, Dirk; Rosenberg, Charles; Roy, Nicholas; Schulte, Jamieson; Schulz, Dirk

doi:10.1177/02783640022067922

Cited by 403 publications

(240 citation statements)

References 81 publications

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“…Monte Carlo localization allowed the Minerva robotic museum guide to operate for 44 km over 2 weeks [71]. More recently it has been used by Nuske et al to localize an AUV relative to a marine structure using a camera [59], exploiting GPU-accelerated image formation to facilitate large particle sets.…”

Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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Accurate navigation is a fundamental requirement for robotic systemsmarine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes

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Section: Simultaneous Localization and Mappingmentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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“…Wheeled robots, for example, have already been deployed as museum tour guides or on large fairs [5], [6], [7], [8]. The main focus in these systems, however, was reliable, collisionfree navigation.…”

Section: Related Workmentioning

confidence: 99%

The humanoid museum tour guide Robotinho

Faber

Bennewitz

Eppner

et al. 2009

RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication

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Abstract-Wheeled tour guide robots have already been deployed in various museums or fairs worldwide. A key requirement for successful tour guide robots is to interact with people and to entertain them. Most of the previous tour guide robots, however, focused more on the involved navigation task than on natural interaction with humans. Humanoid robots, on the other hand, offer a great potential for investigating intuitive, multimodal interaction between humans and machines. In this paper, we present our mobile full-body humanoid tour guide robot Robotinho. We provide mechanical and electrical details and cover perception, the integration of multiple modalities for interaction, navigation control, and system integration aspects. The multimodal interaction capabilities of Robotinho have been designed and enhanced according to the questionnaires filled out by the people who interacted with the robot at previous public demonstrations. We present experiences we have made during experiments in which untrained users interacted with the robot.

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“…There are examples of learning in robots that are non-categorical (e.g., visuo-motor control [35]) and implicitly categorical (e.g., systems that learn to avoid obstacles [36]). Further, localisation and navigation systems can be trained to associate features with locations (e.g., [37]). However, it could be argued that this is only mapping features to categories in a categorisation system that the robot was given, and falls well short of human-style high-level classiÿcation.…”

Section: Where Do Categories Come From?mentioning

confidence: 99%

Embodied categorisation for vision-guided mobile robots

Barnes

Liu

2004

Pattern Recognition

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This paper outlines a philosophical and psycho-physiological basis for embodied perception, and develops a framework for conceptual embodiment of vision-guided robots. We argue that categorisation is important in all stages of robot vision. Further, that classical computer vision is unsuitable for this categorisation, however, through conceptual embodiment, active perception can be e ective. We present a methodology for developing vision-guided robots that applies embodiment, explicitly and implicitly, in categorising visual data to facilitate e cient perception and action. Finally, we present systems developed using this methodology, and demonstrate that embodied categorisation can make algorithms more e cient and robust. Crown

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Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva

Cited by 403 publications

References 81 publications

Simultaneous Localization and Mapping in Marine Environments

Simultaneous Localization and Mapping in Marine Environments

The humanoid museum tour guide Robotinho

Embodied categorisation for vision-guided mobile robots

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