Current Research at AASS Learning Systems Lab

Transcription

1 Current Research at AASS Learning Systems Lab Achim Lilienthal, Tom Duckett, Henrik Andreasson, Grzegorz Cielniak, Li Jun, Martin Magnusson, Martin Persson, Alexander Skoglund, Christoffer Wahlgren Örebro University

2 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings (with Peter Biber)

3 1 Robotic Platform PeopleBot 2D Laserscanner Omnicam Thermal Camera Monocular Camera Gripper

4 1 Robotic Platform

5 1 Robotic Platform

6 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings (with Peter Biber)

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8 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings (with Peter Biber)

9 4 3D Mapping 3D Scan Registration (Martin Magnusson)

10 4 3D Mapping 3D Scan Registration Autonomous Mining Vehicles Project Partners Atlas Copco Rock Drills Optab Optronikinnovation AB Robotdalen

11 4 3D Mapping Experimental Setup 40 cm 7 m

12 4 3D Mapping 3D-ICP pair corresponding (= closest) points Calculate the transformation that minimises the total distance between corresponding points repeat

13 4 3D Mapping 3D-NDT point cloud probability function minimise total distance repeat

14 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings (with Peter Biber)

15 5 Topological Map Building from Omnivision Appearance-based Approach (Omnicam)

16 5 Topological Map Building from Omnivision matching local features (SIFT) grey level image find interest points (corners or patterns) calculate gradient direction and magnitude (32x32 window) histogram of directions and magnitudes (16x8 bins) keypoint descriptor (vector of length 128)

17 5 Topological Map Building from Omnivision Feature Matching (relative match criterion)

18 5 Topological Map Building from Omnivision Results

19 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings

20 6 Dynamic Mapping Remaining Challenges for Mapping and Localisation long-term operation moving people, temporary objects, rearranged furniture large and dynamic environments living together with people Consequences for M&L Algorithms coping with dynamic environments continuous and lifelong learning

21 6 Dynamic Mapping Simple Learn Once Map only one entry: distance is d d d d dynamic map should represent both distances t

22 6 Dynamic Mapping Suggested Solution: Dynamic Map sample-based representation interpretation by robust statistics (median, MAD) updating the dynamic map by replacing samples mean sample lifetime λ maintain map at several time-scales (5 scales) Stability Plasticity λ λ λ λ λ

23 6 Dynamic Mapping Stability Plasticity Oct 25 Oct 26 Oct 29

24 Contents 1) Robotic Platform A. Robotic Map Learning 2) SLAM (Tom Duckett with Udo Frese) 3) Semi-Autonomous Mapping (Martin Persson) 4) 3D Mapping (Martin Magnusson) 5) Topological Mapping (Christoffer Wahlgren) 6) Dynamic Mapping (with Peter Biber) 7) Acquiring Realistic 3D Models of Buildings

25 7 Acquiring Realistic 3D Models of Buildings Joint work with Peter Biber 2D map from laser scans 3D model from 2D laser map texture added by multi-resolution blending

26 7 Acquiring Realistic 3D Models of Buildings

27 7 Acquiring Realistic 3D Models of Buildings

28 Contents B. Learning Control 8) Reinforcement Learning for Time-Optimal Control (with Tomás Martinéz-Marín) 9) Task-Non-Specific Learning of New Skills 10) Teaching by Demonstration (Alexander Skoglund) C. Recognition Systems for Autonomous Robots 11) Vision-Based Recognition of Places (Henrik Andreasson) 12) Detection and Tracking of Multiple Persons (Grzegorz Cielniak) D. Mobile Robot Olfaction

29 8 RL for Time-Optimal Control Visual Servoing and RL (with Tomás Martinéz-Marín) full screen full screen

30 Contents B. Learning Control 8) Reinforcement Learning for Time-Optimal Control (with Tomás Martinéz-Marín) 9) Task-Non-Specific Learning of New Skills (Li Jun) 10) Teaching by Demonstration (Alexander Skoglund) C. Recognition Systems for Autonomous Robots 11) Vision-Based Recognition of Places 12) Detection and Tracking of Multiple Persons (Grzegorz Cielniak) D. Mobile Robot Olfaction

31 12 Person Detection and Tracking Multiperson Tracking full screen

32 12 Person Detection and Tracking Ellipse Model State x ( i) t = ( p, v) = ( x, y, w, h, d, v Dynamic model: random walk x, v y, v w, v h ) full screen full screen

33 12 Person Detection and Tracking Tracking and Following Persons Combined with Face Tracking full screen

34 Current Research at AASS Learning Systems Lab Tom Duckett, Achim Lilienthal Thank you! Örebro University

35 Current Research at AASS Learning Systems Lab Örebro University