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lecture notes - Probabilistic Robotics Probabilistic Motion...

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Unformatted text preview: Probabilistic Robotics Probabilistic Motion Models 2 Robot Motion • Robot motion is inherently uncertain. • How can we model this uncertainty? 3 Probabilistic Motion Models • To implement the Bayes Filter, we need the transition model p(x | x’, u) . • The term p(x | x’, u) specifies a posterior probability, that action u carries the robot from x’ to x . • In this section we will specify, how p(x | x’, u) can be modeled based on the motion equations. 4 Coordinate Systems • In general the configuration of a robot can be described by six parameters. • Three-dimensional cartesian coordinates plus three Euler angles pitch, roll, and tilt. • Throughout this section, we consider robots operating on a planar surface. • The state space of such systems is three- dimensional (x,y, θ ). 5 Typical Motion Models • In practice, one often finds two types of motion models: • Odometry-based • Velocity-based ( dead reckoning ) • Odometry-based models are used when systems are equipped with wheel encoders.are equipped with wheel encoders....
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This note was uploaded on 10/17/2010 for the course CSCI 547 at USC.

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lecture notes - Probabilistic Robotics Probabilistic Motion...

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