1. Biologically-inspired Visual Landmark Navigation for Mobile Robots
Recent Research
Biologically-inspired Visual Landmark Navigation for Mobile Robots
Collaborative Work with Bianco

2. Mobile Robot Navigation
Robot Navigation relies answering the questions:
Where am I?
Where are other places relative to me?
How do I get to other places from here?
Possible answers:
Classical robotic techniques
New trend: biologically-inspired methods

3. Biological-inspiration
Animals and insects are proficient in visual navigation (Papi 1992)
The use of natural visual landmarks by insects for navigation have been well documented (Wehner 1992)
Strategies for the selection of natural landmarks by insects has been reported (Lehrer 1993, Zeil 1993)
Many models have been introduced without formal methods (Trullier et al. 1997)

4. Related Issues Navigation can be considered as a four-level hierarchy
guidance
place recognition-triggered response
topological navigation
metric navigation
We perform guidance: the agent is guided by a spatial distribution of landmarks
we do not use maps
we do not know our position in reference co-ordinates

5. Acquiring Visual Landmarks
“A landmark must be reliable and landmarks which appear to be appropriate for human beings are not necessarily appropriate for robots because of the different sensor and matching apparatus.” Matàric 1990, Thrun 1996
If we can establish what is meant by reliability for given sensors and matching schemes then the problem of landmark selection is automatically solved!
Reliability depends on sensor and matching scheme
Sony NTSC camera and Fujitsu Tracking card (TRV)
Landmarks are based on the image correlation concept.

6. Definition of a Landmark
A landmark is a region within a whole image
TRV performs 16 x 16 SAD correlation
A correlation matrix is generated
(ox,oy)
(ox-8*mx,oy -8*my)
(ox+7*mx,oy +7*my)
16*mx
16*my
Template
Frame

7. Uniqueness of Landmarks
Different landmarks have different correlation matrices

8. Reliability of Landmarks
We define the reliability of a landmark as
where g’ is a local minimum found in the neighbourhood of g, the global minimum.

9. Selecting Landmarks
Maximising r, coupled with different template sizes, we can select unique landmarks.
Magnification size = 3 & 4
Magnification size = 5 & 6
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10. Turn Back and Look How “dynamically” reliable are our landmarks?
Through a phase directly inspired by wasps and bees, the robustness of statically chosen landmarks is tested.
The robot moves with stereotyped movements
The camera continuously points toward the goal
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11. Turn Back and Look Two frames from a typical TBL phase
Numbers show the reliability factors of the landmarks
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12. Turn Back and Look
The reliability r is constantly monitored for each landmark during TBL.
Only those landmarks whose r is above a threshold e are considered.
Small perturbations (light, position, etc.) are produced with TBL and this represents a framework for testing the reliability of real navigation tasks.
“Only strong individuals can survive through a selection phase” (Murray Gell-Mann, The Quark and the Jaguar, 1994)
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13. Perturbations
TBL produces small perturbations: light, perspective, size...
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14. Landmark Navigation
The underlying principle is based on the model proposed by Cartwright and Collet to explain bee behaviour.
It mimics the behaviour of a bee quite well BUT a 2D extension is required
Key Point for the extension:
A landmark is attracted toward its original position and size
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15. Landmark Navigation
Displacement from the original position and size is suitable for extracting navigation information from landmarks.
Original position and size New position and size
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16. Landmark Navigation
Let be the difference between the original and present positions of the landmarks.
Let be the weight for size difference of the landmarks.
The landmark attraction vector is given by:
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17. Landmark navigation By fusing all the landmark attraction vectors
through weighted averaging:
we obtain the final
navigation vector
Typical image input frame
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18. Images from a Navigation Experiment
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19. Landmark navigation
A navigation vector field: for each (x,y) can be computed
21656
5234
2294
3742
238
5623
4899
1542
2774 95
193
207
134
405 17
196 72
1340
121
192
3362
909
5358
4431
4551
1076
4032
3450
1017
5733
13172
4717
5023
5702
655
1025
1155
6824
16777
5596
16863
15323
14001
14526
12444
3503
7588
5228
8002
5210
9118
981
2263
2047
2240
2341
3768
12717
1853
4243
8675
5208
5934
6891
1225
13512
11857
7510
3006
2134
1307
2413
22001
1020 cm
60 cm
GOAL POSITION
720 cm
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20. Visual Potential Field
There is evidence of a potential field when biologically-based navigation is considered (Voss 1995, Gaussier 1998)
In this case, a potential function U(x,y) such that
can drive the movements of the robot.
A necessary and sufficient condition for U to exist is that the vector field is conservative, that is:
or, alternatively
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21. Computation of Partial Derivatives
Partial derivatives and are computed numerically.
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22. TBL affects Conservativeness
TBL affects the conservativeness according to different thresholds
Plotting for different threshold values of e yields:
e= e=0.1
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23. TBL affects Conservativeness
e= e=2.5
As e -->1 the vector field becomes conservative and computation of the potential field can be possible
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24. Computation of the Potential Field
Different Potential Fields U can be generated from values of e.
e=0 e=0.1
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25. Computation of the Potential Field
e= e=0.25
As e -->1 the potential field is suitable to drive navigation
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26. Visual potential field
When a smaller template size is considered, the potential field basin has a different shape:
deeper at the goal position.
a reduced basin of attraction.
Example size 4, e=0.2
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27. Experimentation Equipment Basic navigation rule: Nomad200 Sony EVI-D30
Fujitsu Colour TRV
Basic navigation rule:
if then
continue using the last navigation vector
else
give the robot the currently computed navigation vector
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28. The Environment December 1999 28 1020 cm 60 cm 720 cm GOAL POSITION
CUPBOARD(h=210cm)
TABLE (h=70cm)
60 cm
GOAL POSITION
COLUMN
720 cm
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29. Experiment A Size = 6 Threshold e = 0 December 1999 29 1020 cm 60 cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60 cm
GOAL POSITION
COLUMN
720 cm
WALL 1 G1 2 3 4 G4 5 G5 6 7 8 G8 9 G9 10
G10 11
G11
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30. Experiment B Size = 6 Threshold e = 0.2 December 1999 30 1020 cm 60 cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60 cm
GOAL POSITION
COLUMN
720 cm
WALL 1 G1 2 G2 3 4 G4 5 G5 6 7 8 G8 9 G9 10
G10 11
G11 G6 G7
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31. Experiment C Size = 5 Threshold e = 0.2 December 1999 31 1020 cm 60 cm
CUPBOARD(h=210cm)
TABLE (h=70cm)
60 cm
GOAL POSITION
COLUMN
720 cm
WALL 1 G1 2 G2 3 4 G4 5 G5 6 7 8 G8 9 G9 10
G10 11 G6 G7
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32. Conclusions Major results Most importantly
Self-selection of natural landmarks
Theory of visual potential
Landmark definition based on reliability
Landmark navigation can been formalised as driven by a potential field
Invariants or Transformations are not needed
Most importantly
TBL affects the conservativeness of the vector field
strong landmarks = conservativeness = potential field
Biologically-inspired navigation methods are effective
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