Portland Area Robotics Society

Regular Meeting Minutes

April 5, 2008, 10:30AM 


1.     Welcome

2. Presentation

- Introduction to Bayesian Particle Filters

3. Announcements 

- BotFest 08

4. Update on Open AHRMS

5.      Show and Tell



Introduction to Bayesian Particle Filters Professor Barton C. Massey, Portland State University


Authors E-mail - bart@cs.pdx.edu

Authors Website - http://web.cecs.pdx.edu/~bart/



1.      What's the problem?

Why do we need to know state of Robot? Platform Control, Accurate Navigation


One Solution - Bayesian Particle Filtering (BPF)

A method of Sensor Fusion Robot State Space tracking

BPF is the HOT NEW Alternative to Kalman Filtering Last 10-15 years


2. Approaches

a) Deal with sensor unreliability by adding redundant sensors -

b) Kalman - linear normal distribution estimation of maximum probability position

Linear algebra - lots of high level math required

Improvements - EKF, UKF

c) Bayesian Particle Filtering - Computational intensive


3. Bayesian Particle Filtering - How does it work?

BPF Inputs

State of vehicle - s

g: dead reckoning

h: measurement function


Need to deal with multiple probabilities from Dead Reckoning and sensor measurement. In order to find robot location you must maximize the product of all state probabilities. Sounds Simple, Right? Space is large


Basic Process for BPF:

0) Create copies of vehicle state, Si. Set all probabilities to 1 (Wi = 1)

1) Propagate each state copy, Si, forward one time of sample.

2) Calculate the probability of each state (Si) given the sensor reading (h) and probability, Wi.

3) Pick and Si to use right now based on maximum probability product, Wi[max]

4) Resample Si. Use a weighted random selection of states to continue duplication and propagation.

5) Repeat for next time sample.


Wi' = h(Si)*Wi [normalize]


Bayesian Particle Filter is a method of simulated evolution. BPF is a useful machine learning technique.


Drawback - Computationally intensive - Floating Point can be a plus


BotFest '08 Indoor Competition May 17th at the Portland Children's Museum (Cafe Space)



BotFest 08 Outdoor Competition May 25th at the Oregon Episcopal School (OES)




New PARTS Website New Content Management System



OpenAHRMS Update


OpenAHRMS is the Open Source project to develop an Attitude and Heading Reference and Measurement System for Personnel Robotics. Hardware layout is nearing completion. The current design will provide USB connectivity to Host controller.




Show and Tell