1. SLAM – Loop Closing with Visually Salient Features
Paul Newman, Kin Leong Ho
Oxford University Robotics Research Group

2. Motivation
Loop Closing – the task of deciding whether a vehicle has returned to a previously visited area
Popular approaches – nearest neighbour statistical gate, joint compatibility test
Closing loop with visually salient features to avoid dependence on global position estimate

3. Visual Saliency Scale saliency detector [ Kadir/Brady IJCV 2001]
- form p.d.f. of pixel properties within local region at varying scales for each pixel
detection of region at a particular scale where weighted entropy is peaked
selected regions are considered more “interesting”
p.d.f of local pixels over scale s around position x
entropy

4. Visual Saliency Dissimilarity p.d.f of across scale Weighting Function
Saliency metric
entropy
Weighting function

5. Wide-Baseline Stability
Maximally stable extremal region (MSER) detector [ Matas etal. BMVC 2002]
-pixels taking on values in the range D = {dmin ….dmax}

6. MSER detector
Saliency detector

7. Feature Description -Scale invariant feature
transform (SIFT) descriptor
[David Lowe IJCV 2004]
-128 dimensional descriptor

8. MSER
Detector
Query
Image
Selected
Regions
SIFT
Descriptor
Saliency
Detector
Similarity
Measure
Matched
Images
Laser Scan
Database
Image
Database

9. Demonstration of wide-baseline stability of visually salient features under perspective distortion and variation in illumination conditions

10. Matching Performance
Similar posters found in the environment.

12. A Delayed State Formulation
Control
Past poses
Scan matching between
Past poses produces observation z with which to update state-vector
State vector contains only past vehicle poses. (Atlas IJRR 2004 )

13. Delayed State Formulation II
EKF update

14. Closing Small Loops

15. Closing Big Loops

16. Closing the loop

17. Issues -hard decision making -using saliency detector
as binary selector
-repetitive visual features
in urban environment

18. Demonstration

19. Questions
Thank you!