Recursive Bayes Filters and related models for mobile

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Recursive Bayes Filters and related models for mobile robots

Recursive Bayes Filters and related models for mobile robots

Recursive Bayes Filters We will briefly review our derivation of Bayes filter from one

Recursive Bayes Filters We will briefly review our derivation of Bayes filter from one of previous lectures first.

Two steps of Bayes filter: Prediction and Correction

Two steps of Bayes filter: Prediction and Correction

Two steps of Bayes filter: Prediction and Correction control From odometry and equations of

Two steps of Bayes filter: Prediction and Correction control From odometry and equations of motion • Use measurement to correct

 • Both the prediction step and the correction step use the following: –

• Both the prediction step and the correction step use the following: – Motion model – Sensor or observation model

Formulas from previous slide

Formulas from previous slide

Different Realizations of Bayes Filters • Recursive filters

Different Realizations of Bayes Filters • Recursive filters

Main Approaches to Bayes Filters • Similar methods based on Bayesian probability, networks, and

Main Approaches to Bayes Filters • Similar methods based on Bayesian probability, networks, and evolutionary algorithms also exist

Probabilistic Motion Models

Probabilistic Motion Models

Using only odometry in long run is definitely wrong

Using only odometry in long run is definitely wrong

Explain the meaning

Explain the meaning

 • In past we used velocity models for simple Braintenberg Vehicles • For

• In past we used velocity models for simple Braintenberg Vehicles • For MCECSBOT we will have to use perhaps the odometry-based model

Motion Model based on ODOMETRY

Motion Model based on ODOMETRY

Motion Model for a robot based on ODOMETRY • This model will be more

Motion Model for a robot based on ODOMETRY • This model will be more complicated for OMNI and MECCANO WHEELS

Probability Distribution in Motion Model for a robot based on ODOMETRY

Probability Distribution in Motion Model for a robot based on ODOMETRY

Examples of Odometry-Based noise

Examples of Odometry-Based noise

Velocity Based Motion Models for a robot

Velocity Based Motion Models for a robot

Velocity Based Motion Models for a robot Explain the meaning • • It is

Velocity Based Motion Models for a robot Explain the meaning • • It is easy to derive such model for a two-wheeled robot We have done it as part of kinematics explanation in Fall quarter (for non-deterministic case).

Motion Equation for Velocity Based Motion Models for a robot

Motion Equation for Velocity Based Motion Models for a robot

 • We add an additional noise term now.

• We add an additional noise term now.

 • Here we fix the problem outlined in the previous slide

• Here we fix the problem outlined in the previous slide

The dark clouds represent probability density Moving on circles The dots represent samples of

The dark clouds represent probability density Moving on circles The dots represent samples of probability

Sensor Models

Sensor Models

Sensor model for Laser Scanners

Sensor model for Laser Scanners

 • You do not need to know too much if you are small

• You do not need to know too much if you are small and only want to move straight ahead

Ray Cast Sensor Model

Ray Cast Sensor Model

Feature-based Model for Range-Bearing Sensors error

Feature-based Model for Range-Bearing Sensors error

Summary • Bayes Filter is a framework for state estimation • Motion model and

Summary • Bayes Filter is a framework for state estimation • Motion model and sensor model are the central models in Bayes Filter • These are all standard models for: – Robot motion – Laser-based range sensing – Similar sensors