SIMULTANEOUS LOCALIZATION AND MAPPING FOR EVENT-BASED VISION SYSTEMS

Transcription

1 SIMULTANEOUS LOCALIZATION AND MAPPING FOR EVENT-BASED VISION SYSTEMS David Weikersdorfer, Raoul Hoffmann, Jörg Conradt 9-th International Conference on Computer Vision Systems 18-th July 2013, St. Petersburg, Russia

2 Outline 1. Event-based Vision with Dynamic Vision Sensors 2. Event-based Particle Filtering 3. Event-based Simultaneous Localization and Mapping 4. Applications and Results 2013/07/18 Page 2

3 Classic vs. Event-based Vision 2013/07/18 Page 3

4 Event-based Vision Pixels individually react to change in illumination Stream of pixel coordinates No frames! Sparse data stream Efficient algorithms Page 4

5 Dynamic Vision Sensors

6 Dynamic Bayesian Network Temporal development of random variable Based on observations void Page 6

7 Dynamic Bayesian Network Temporal development of random variable Based on observations void Condensation Algorithm (Isard and Black, 1998) Page 7

8 Dynamic Bayesian Network Temporal development of random variable Based on observations void Condensation Algorithm (Isard and Black, 1998) Page 8

9 Event-based Particle Filtering Event-based vision from edvs Page 9

10 Event-based Particle Filtering Problem 1: Too many measurements ~20,000 events/sec Page 10

11 Event-based Particle Filtering Problem 1: Too many measurements ~20,000 events/sec Solution: Do not resample after every event Page 11

12 Event-based Particle Filtering Problem 2: Measurements not independent Page 12

13 Event-based Particle Filtering Problem 2: Measurements not independent Solution: Use exponential decay Page 13

14 Event-based Particle Filtering Problem 2: Measurements not independent Solution: Use exponential decay D. Weikersdorfer, J. Conradt, Event-based particle filtering for robot self-localization, ROBIO 2012 Page 14

15 Event-based SLAM Simultaneous Localization and... Page 15

16 Event-based SLAM Simultaneous Localization and Mapping Page 16

17 Event-based Mapping Dynamic map over 2D map space : Occurrence map : Normalization map Occurrence map Normalization map Final map Page 17

18 Event-based Mapping Dynamic map over 2D map space Occurrence map: : Gaussian normal distribution : Projection of event onto map given a state estimate Occurrence map Normalization map Final map Page 18

19 Event-based Mapping Dynamic map over 2D map space Occurrence map: : Gaussian normal distribution : Projection of event onto map given a state estimate Occurrence map Normalization map Final map Page 19

20 Event-based Mapping Dynamic map over 2D map space Normalization map: : Total state estimate at step k : Projection of map location onto retina given a state Occurrence map Normalization map Final map Page 20

21 Event-based Mapping Dynamic map over 2D map space Normalization map: : Total state estimate at step k : Projection of map location onto retina given a state Occurrence map Normalization map Final map Page 21

22 Results: Event-based SLAM (2D) Results from event-based SLAM Comparison to ground truth (Red: estimated path, Blue: overhead tracker) Position and rotation error Page 22

23 Results: Event-based SLAM (2D) Results from event-based SLAM Map estimate from SLAM Comparison to ground truth Map comparison Position and rotation error Map ground truth (photo of ceiling) Page 23

24 Results: Event-based SLAM (2D) Page 24

25 Results: Autonomous Exploration Page 25

26 Conclusion Dynamic Vision Sensors Sparse data stream of pixel events Event-based Particle Filtering Adaptation of Condensation to event-based vision Event-based SLAM Low bandwidth for embedded systems 2013/07/18 Page 26

27 References D. Weikersdorfer, J. Conradt, Event-based particle filtering for robot selflocalization, ROBIO 2012 D. Weikersdorfer, R. Hoffmann, J. Conradt, Simultaneous Localization and Mapping for event-based Vision Systems, ICVS 2013 R. Hoffmann, D. Weikersdorfer, J. Conradt, Autonomous Indoor Exploration with an Event-Based Visual SLAM System, ECMR 2013 Contact: David Weikersdorfer, Neuroscientific System Theory (NST) 2013/07/18 Page 27