Robot Compagnion Localization at home and in the office Arnoud Visser, Jürgen Sturm, Frans Groen University of Amsterdam Informatics Institute

Overview Mobile robotics Mobile robotics Robot localization Robot localization Presentation of the panorama approach Presentation of the panorama approach Results Results Demonstration videos Demonstration videos

Mobile robotics SICO at Kosair Children's Hospital Dometic, Louisville, Kentucky Sony Aibos playing soccer Cinekids, De Balie, Amsterdam Robot cranes and trucks unloading ships Port of Rotterdam RC3000, the robocleaner Kärcher

The localization problem Robot localization Robot localization.. is the problem of estimating the robot’s pose relative to a map of the environment. Position tracking Position tracking Global localization Global localization Kidnapping problem Kidnapping problem

Localization Sensors Sensors Odometry, GPS, Laserscanner, Camera.. Odometry, GPS, Laserscanner, Camera.. Feature space Feature space World representation World representation Topological graphs, grid-based maps Topological graphs, grid-based maps Filters Filters Kalman filters, particle filters Kalman filters, particle filters

Classical approaches Special environments Special environments (Visual) landmarks (Visual) landmarks (Electro-magnetic) guiding lines (Electro-magnetic) guiding lines Special sensors Special sensors GPS GPS Laser-scanners Laser-scanners Omni-directional cameras Omni-directional cameras Special requirements Special requirements Computationally heavy (offline computation) Computationally heavy (offline computation)

New approach Natural environments Natural environments Human environments Human environments Unstructured and/or unknown for the robot Unstructured and/or unknown for the robot Normal sensors Normal sensors Camera Camera Reasonable requirements Reasonable requirements Real-time Real-time Moderate hardware requirements Moderate hardware requirements

Platform: Sony Aibo Internal camera 30fps 208x160 pixels Computer 64bit RISC processor 567 MHz 64 MB RAM 16 MB memorystick WLAN Actuators Legs: 4 x 3 joints Head: 3 joints

Demo: Compass Library, University of Amsterdam

Synopsis

Color segmentation Sidetrack: Color Calibration Robot collects colors from environment Robot collects colors from environment Colors are clustered using an EM algorithm Colors are clustered using an EM algorithm Color-to-Colorclass lookup table is created for faster access Color-to-Colorclass lookup table is created for faster access Raw image Color class image

Mathematics rotation translation feature vector ideal world model learned world model

Feature space conversion

Feature vectors and world model World model distribution Feature vector consists of color transition counts between the n color classes

Feature space conversion (2) Raw image Color class image Sector-based feature vectors

Learning Update distribution of single color class transition by updating the constituting counters

Matching Likelihood of Single sector Rotation estimate Confidence estimate Adjacent sectors

Post-processing: Compass Idea: smooth rotational estimate over multiple frames + removes outliers + stabilizes estimate + integrates (rotational) odometry

Results: Compass Brightly illuminated living room

Results: Compass Daylight office environment

Results: Compass Outdoor soccer field

Results: Compass Robocup 4-Legged soccer field

Signal degradation (w.r.t. distance) Robocup 4-Legged soccer field

Post-processing: Grid localization Idea: learn multiple spots, then use confidence value to estimate the robot‘s position in between – fixed grid (better: self-learned graph based on confidence) – difficult to integrate odometry + proof of concept

Demo: Grid localization Robocup 4-Legged soccer field

Results: Grid localization Robocup 4-Legged soccer field x [cm] y [cm] Positioning accuracy Robot walks back to center after kidnap

Conclusions Novel approach to localization: Novel approach to localization: Works in unstructured environments Works in unstructured environments Tested on various locations Tested on various locations Interesting approach for mobile robots at home and in the office Interesting approach for mobile robots at home and in the office

Questions?