1. TechUnited Robocup – the object tracking problem René van de Molengraft MSL Workshop, Stuttgart, November 5/6 th, 2009

2. 2/26 Looking back No joint paper  two-day visits Stuttgart and Tribots Special issue IFAC Mechatronics on MSL EU-Project RoboEarth

3. 3/26 Evolution and revolution Devbot0 2005 Model 2006 Model 2007 Model 2008 Model 2009

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5. 5/26 5 against 5 >30 kg, 4 m/s omni-vision: 30 x 640 x 480 x 3 bytes/s assume features (x, y, r) from omni- vision noise (shadow, motion blur, finite resolution) and multiple features/object

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7. 7/25 On the field

8. 8/26 Object tracking 1 2 time 1 1 2 time 2 data association find best state estimates given past position measurements

9. 9/26 Combinatorial explosion 1 2 5 3 4 6 at time t1: m1 measurements, n1 objects m1*n1 possible associations at next time t2: m1*n1*m2*n2

10. 10/26 State estimation 2-D position measurements from vision constant velocity model stationary Kalman filter

11. 11/26 Object model zero-acceleration

12. 12/26 Kalman filter choose uncertainty weigths solve Riccati equation -> K variance equation is disregarded

13. 13/26 Sequential clustering using Kalman filters inspired by Schubert and Sidenbladh, Sequential clustering with particle filters – estimating the number of clusters from data, 2005 prune exploding tree by particle filtering static objects only

14. 14/26 Sequential clustering using Kalman filters prune exploding tree by discrete filtering track dynamic objects incorporate a-priori knowledge

15. 15/26 Sequential clustering using Kalman filters measurements are processed sequentially measurement can be associated with clutter new object existing object

16. 16/26 Tree with all possible associations [] [0] [1] [0,0][0,1] [1,0] [1,1][1,2] measurement 1 measurement 2 1, 2, 5, 52, 203, 877, 4140, 21147, 115975, 678570

17. 17/26 Hypothesis H e.g. H= [0, 1, 1, 2, 1, 2, 3, 0, 1, 1, 1, 4, 2] so, 4 objects -> 4 Kalman filters each hypothesis is assigned a probability propagate hypotheses via Bayesian filtering

18. 18/26 Prediction

19. 19/26 Correction is the position of the object in assigned to measurement k

20. 20/26 Pruning by discrete filter set of hypotheses form a discrete distribution select n hypotheses with highest probability throw away hypotheses with P<0.01*Pmax tree is pruned to at most n hypotheses n = 10 still works for 10 to 20 objects!

21. 21/26 A-priori knowledge region of interest clip beyond boundary in state space measurements objects in hypotheses maximum number of objects in hypothesis

22. 22/26 Maximum a Posteriori (MAP) estimate states Best estimate

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24. 24/26 Implementation Simulink S-function in C cpu effort linear in #measurements linear in #hypotheses quadratic in #objects < 5 ms (10, 20, 10) add predictor for anticipation