cs685-perception-1

cs685-perception-1 - 10/15/09 Perception Sensors...

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10/15/09 1 Perception Sensors Uncertainty Features Introduction Chapter 4 [Nourbaksh & Siegwart] Introductory slides (courtesy [Nourbaksh & Siegwart]) Example Robart II, H.R. Everett
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10/15/09 2 BibaBot, BlueBotics SA, Switzerland Pan-Tilt Camera Omnidirectional Camera IMU Inertial Measurement Unit Sonar Sensors Laser Range Scanner Bumper Emergency Stop Button Wheel Encoders Robotic Navigation Stanford Stanley Grand Challenge Outdoors unstructured env., single vehicle Urban Challenge Outdoors structured env., mixed traffic, traffic rules
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10/15/09 3 Terrain mapping using lasers Determining obstacle course Classification of Sensors Proprioceptive sensors measure values internally to the system (robot), e.g. motor speed, wheel load, heading of the robot, battery status Exteroceptive sensors information from the robots environment distances to objects, intensity of the ambient light, unique features. Passive sensors energy coming for the environment Active sensors emit their proper energy and measure the reaction better performance, but some influence on environment 4.1.1
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10/15/09 4 Jana Kosecka Role of Perception in Robotics Where am I relative to the world? sensors: vision, stereo, range sensors, acoustics problems: scene modeling/classification/recognition integration: localization/mapping algorithms (e.g. SLAM) What is around me? sensors: vision, stereo, range sensors, acoustics, sounds, smell problems: object recognition, structure from x, qualitative modeling integration: collision avoidance/navigation, learning Role of Perception in Robotics How can I safely interact with environment (including people!)? sensors: vision, range, haptics (force+tactile) problems: structure/range estimation, modeling, tracking, materials, size, weight, inference integration: navigation, manipulation, control, learning How can I solve “new” problems (generalization)? sensors: vision, range, haptics, undefined new sensor problems: categorization by function/shape/context/?? integrate: inference, navigation, manipulation, control, learning Jana Kosecka
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10/15/09 5 Jana Kosecka About 60% of our brain is devoted to vision We see immediately and can form and understand images instantly Detailed representations are often not necessary Different approaches in the past Animate Vision (biologically inspired), Purposive Vision (depending on the task/purpose) Visual Perception Topics range sensing, Obstacle detection, environment interaction Mapping, registration, localization, recognition Manipulation • Computational Stereo • Feature detection and matching • Motion tracking and visual feedback Techniques Applications in Robotics:
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6 Jana Kosecka Image Brightness values I(x,y) Image - Apperance J. Kosecka, GMU Image Formation Pinhole Frontal pinhole
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This note was uploaded on 04/07/2010 for the course CS 685 taught by Professor Luke,s during the Fall '08 term at George Mason.

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cs685-perception-1 - 10/15/09 Perception Sensors...

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