1. From Cognitive Spatial Mapping to Robot Mapping Margaret Jefferies University of Waikato New Zealand Hans-Wissenschaftskolleg University of Bremen Germany

2. Autonomous Mobile Robots

6. Robots might not be taking over the world any time soon but they could soon rule the roost if most New Zealanders have their way. More than two-thirds of New Zealanders would welcome robots to do chores around the house, according to a study of 750 people, commissioned by Honda. Most people wanted robots to help with housework, many wanted an extra mechanical hand with the washing up and some wanted a robot to mow the lawns.

7. Politicians and the All Blacks need to watch their backs – some respondents suggested robots should replace politicians and that a team of robots might fare better than the present rugby team. Some people said they would even swap their partners for robots. Women were keener for a robotic partner, with 5.5 per cent saying they would like to switch, compared with just 3.3 per cent of men wanting to replace their partner.

8. Autonomous Mobile Robots Mapping Robot computes its own map from it own experience of its environment with its imperfect sensors and imperfect odometry What’s the problem?

9. Demo

10. Simultaneous Localisation and Mapping (SLAM) Robot needs to estimate its location at the same time it is estimating its map

11. The localisation problem

14. Approaches Absolute Metric Mapping (Global metric mapping)Absolute Metric Mapping (Global metric mapping) Topological Mapping (Local metric maps)Topological Mapping (Local metric maps)

15. Representation Global Maps Global evidence-grid approachGlobal evidence-grid approach

17. Global metric map

19. The Correspondence Problem (Closing the Cycle)

20. Topological Representations

22. The Correspondence Problem (Closing the Cycle)

23. From Cognitive Spatial Mapping to Robot Mapping Cognitive Map An agents (human animal or robot’s) memory of the spatial environment

24. From Cognitive Spatial Mapping to Robot Mapping Draw inspiration from the way in which humans and animals solve similar problemsDraw inspiration from the way in which humans and animals solve similar problems Study the way humans and animals solve similar spatial mapping problems (to robots)Study the way humans and animals solve similar spatial mapping problems (to robots)

25. The Local Space The space that appears to enclose the viewerThe space that appears to enclose the viewer Initial notion of “where am I”Initial notion of “where am I” A container where objects are located and where actions take placeA container where objects are located and where actions take place

26. Bounded Space O’Keefe and Burgess Nature 1996 - hippocampus

27. Bounded space Russell Epstein and Nancy KanwisherRussell Epstein and Nancy Kanwisher –Nature (1998), Neuron (1999) Parahippocampus encodes the layout of the local space – the enclosed spaceParahippocampus encodes the layout of the local space – the enclosed space

28. Bounded space Environmental Psychologists / GeographersEnvironmental Psychologists / Geographers 1980’s work of the Kaplans1980’s work of the Kaplans Stamps and Smith (2004)Stamps and Smith (2004)

29. The Local Space is Geometric Ken ChengKen Cheng –Cognition (1986) Margules and GallistelMargules and Gallistel –Animal Learning and Behavior(1988) Huttenlocher et alHuttenlocher et al –Cognitive Psychology (1979), (1994) Hermer and SpelkeHermer and Spelke –Nature (1994), Cognition (1996)

30. Exits are important Evolutionary psychologistsEvolutionary psychologists –Kaplans (1980s) –Laslo et al “the Evolution of Cognitive Maps” (1993) Environmental psychologistsEnvironmental psychologists –Herzog (2001 – 2004) –Visual access

31. The Local Mapping Approach

32. Occlusion Map Local space representation

34. Putting it all together The Theory of Siegel and White has dominated thinking in this area since it was first proposed in 1975The Theory of Siegel and White has dominated thinking in this area since it was first proposed in 1975 landmark route / topological survey global metric Most computational cognitive mapping approaches use all of these

35. Putting it all together The Theory of Siegel and White has dominated thinking in this area since it was first proposed in 1975The Theory of Siegel and White has dominated thinking in this area since it was first proposed in 1975 landmark route / topological survey global metric Most computational cognitive mapping approaches use all of these

36. Local space Topological Map Global Metric Map

38. Demo

39. Detecting Cycles in a Global Metric Map

41. Need to figure out if a newly encountered local space is already in the topological mapNeed to figure out if a newly encountered local space is already in the topological map Need to account for the uncertainty in local spaceNeed to account for the uncertainty in local space –In particular occlusion Want to do it quicklyWant to do it quickly Closing Cycles in a Topological Map 2D Landmarks

42. Find (eventually) a signature that identifies the the local space from wherever it is approachedFind (eventually) a signature that identifies the the local space from wherever it is approached Learn what it is that makes each local space different from all the othersLearn what it is that makes each local space different from all the others Whenever we compute a new local space we match it against these signaturesWhenever we compute a new local space we match it against these signatures

43. Signature learning A backprop Neural Network Feature selection Input values are discretised into intervals (200mm) and 45 o Classification – Output values between 0 and 1 indicate the degree of similarity

44. 1 1 2 3 4 5 6 7 8 9 10 11 Matches 2

45. Local space 1234567891011 Prediction.78.94.89.71-.11.72.18.51.34.36.04 2* 1 2 3 4 5 6 7 8 9 10 11

46. Disadvantage NN doesn’t tell us how the local spaces match just that they do.NN doesn’t tell us how the local spaces match just that they do. Need to find the connectivityNeed to find the connectivity

47. ASR12345678910 Prediction.46.97.91.48.64.26.57.88.15.77 1 2 3 4 5 6 7 8 9 10 Best prediction is for 2 Should be 3

48. 3D Visual Landmarks

51. Conclusion Recognising places they have been to before is a hard problem for robotsRecognising places they have been to before is a hard problem for robots There is no perfect solution!There is no perfect solution! Then there is the dynamics of the environment to contend withThen there is the dynamics of the environment to contend with