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Markovito’s Team (INAOE, Puebla, Mexico)

Markovito’s Team (INAOE, Puebla, Mexico). Team members. Sabina : hardware platform. PeopleBot Laser SICK LMS200 PTZ system - video camera VCC5 Two rings of sonars and infrared sensors Stereo vision (videre) Directional microphone and speakers Gripper 2 D.O.F and bumpers. Map building.

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Markovito’s Team (INAOE, Puebla, Mexico)

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  1. Markovito’sTeam(INAOE, Puebla, Mexico)

  2. Team members

  3. Sabina: hardware platform • PeopleBot • Laser SICK LMS200 • PTZ system - video camera VCC5 • Two rings of sonars and infrared sensors • Stereo vision (videre) • Directional microphone and speakers • Gripper 2 D.O.F and bumpers

  4. Map building The initial map is built integrating laser and sonar scanners with particle filters, represented as a probabilistic grid Visual features (SIFT) are integrated to the map for improving localization

  5. Navigation and Localization My flexible and robust navigation algorithm combines an initial plan based on PRMs with a reactive navigator that uses TOPs learned from examples Global and local localization is based on natural landmarks: corners and walls (laser), and SIFT (vision)

  6. Face recognition SIFT feature extraction Localization and tracking Face recognition Video streaming Results

  7. Identification based on silhouettes People identification uses stereo vision and is based on distance and silhouettes models Can identify people standing or sitting, facing forward or seen from the side

  8. Voice recognition, synthesis and animated face My animated face can express different emotions and it is synchronized with my speech Speech synthesis and recognition uses standard tools combined with text processing, directed by the coordinator according to the task

  9. Object Manipuation The Katana arm provides to Sabina with object manipulation capabilities Rapidly Exploring random trees were implemented for motion planning in order to reach a grasping configuration.

  10. Coordinator - MDP The coordination of the different modules to perform certain task is based on a Markov decision process (MDPs) According to each task in this competition, the reward function of the MDP is defined, and by solving the MDP an optimal policy is obtained

  11. Architecture:Modular, Layered, Distributed … Decision Level Who’s who Lost & Found Follow Me SharedMemory plugin’s Level … Objects Voice Naviga- tion Locali- zation Gestures Faces Silhouettes Execution Level Sensors Actuators

  12. Bye…

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