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A generative traversability model for monocular robot self-guidance Michael Sapienza and Kenneth P. Camilleri Dept. Systems and Control Engineering University of Malta

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Sapienza et al (Dept. SCE)

Autonomous Visual Guidance

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Why? Explore dangerous/unknown environments Assist in household/office chores Intreact with us: Human Robot Interaction (HRI) Exploration

HRI Sapienza et al (Dept. SCE)


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Outline 1

Introduction Computer Vision Monocular Camera Motivation Problem Definition



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Outline 1


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Previous Work Traversability Detection Objectives and Contributions



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Methods Feature Extraction Classification Vision Algorithm Locomotion



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Outline 1


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Experimental Results



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Conclusion Sapienza et al (Dept. SCE)


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Introduction

Computer Vision

Computer Vision “Vision is the process of discovering from images what is present in the world, and where it is”



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Introduction

Computer Vision

Computer Vision “Vision is the process of discovering from images what is present in the world, and where it is”

192 164 153 178 181 91 1 54 138 177 201 195 99 103 56 78 154 1 92 101 123 167 165 154 163 19 127 196 4 155 191 154 19 99 33 103 56 78 154 192 43

“If vision is really an information processing task, then I should be able to make my computer do it...” [Marr, 1982] Sapienza et al (Dept. SCE)


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Introduction

Monocular Camera Motivation

Monocular Camera Motivation Camera Advantages Passive, low-cost Actions driven by environment semantics Permits communication with robot through images VISAR01

Single Image Advantages Lower processing cost compared to stereo-vision Single images contain enough information for navigation



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Introduction






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Introduction






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Introduction






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Introduction






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Introduction

Problem Definition

Problem Definition Traversability Detection Finding a set of pixels that define the boundary between traversable ground regions and obstacle regions.

What does it involve? Binary Image Segmentation, the segmentation of an image into two classes: [0,1]. Sapienza et al (Dept. SCE)


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Previous Work

Traversability Detection

Traversability Detection MIT: Pebbles Robot, [Lorigo et al., 1997] Unstructured environments, safe window, Patch based, threshold on histogram matching.



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Previous Work


Traversability Detection MIT: Pebbles Robot, [Lorigo et al., 1997] Unstructured environments, safe window, Patch based, threshold on histogram matching. CMU: Robotic Wheelchair, [Ulrich & Nourbakhsh, 2000] Indoor environments, safe window, Pixel based, histogram thresholding.



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Previous Work


Traversability Detection MIT: Pebbles Robot, [Lorigo et al., 1997] Unstructured environments, safe window, Patch based, threshold on histogram matching. CMU: Robotic Wheelchair, [Ulrich & Nourbakhsh, 2000] Indoor environments, safe window, Pixel based, histogram thresholding. Cranfield University, [Katramados et al., 2009] Outdoor environments, safe window, Pixel based, temporal histogram model.



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Previous Work


Traversability Detection MIT: Pebbles Robot, [Lorigo et al., 1997] Unstructured environments, safe window, Patch based, threshold on histogram matching. CMU: Robotic Wheelchair, [Ulrich & Nourbakhsh, 2000] Indoor environments, safe window, Pixel based, histogram thresholding. Cranfield University, [Katramados et al., 2009] Outdoor environments, safe window, Pixel based, temporal histogram model. Georgia Institute of Technology, [Kim et al., 2007] Outdoor environments, self-supervised, Superpixel based, probabilistic model. Sapienza et al (Dept. SCE)


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Previous Work

Objectives and Contributions

Objectives and Contributions Project Goals Design real-time vision framework to allow a mobile robot to guide itself in an unknown environment using a low resolution monocular camera.

Contrubutions Complementary set of colour and texture features. Smaller safe-window to allow movement in close proximity with obstacles. Novel generative approach which models the feature dissimilarity distribution. Underlying Assumptions Initially, a safe region in front of the robot is traversable. The ground and obstcales can be differentiated from their appearance.



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Previous Work






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Previous Work






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Previous Work






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Previous Work






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Methods

Feature Extraction

Descriptive Features Colour Illumination invariant colour features: Hue & Saturation from HSV colour space. Combination of channels from YCbCr & LAB.



HSV Colour cone

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Methods

Feature Extraction

Descriptive Features Colour Illumination invariant colour features: Hue & Saturation from HSV colour space. Combination of channels from YCbCr & LAB.

HSV Colour cone

Texture Colour is not always reliable eg: white walls; different objects with similar colours. Texture featrues look at the pixel intensity in relation to its neighbours: Edge magnitudes & Orientation patterns. Local Binary Patterns. Calculating the LBP8,1 code Sapienza et al (Dept. SCE)


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Methods

Feature Extraction

Classification primitives Pixels May result in noisy/spotty classification. Pixels



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Methods

Feature Extraction


Patches Allows local feature distributions to be extracted. May contain multiple object boundaries.



Patches

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Methods

Feature Extraction


Patches Allows local feature distributions to be extracted. May contain multiple object boundaries.

Patches

Superpixels Groups of homogeneous pixels: Computationally efficient Preserves image structure Allows rich statistics to be extracted from perceptually meaningful regions Sapienza et al (Dept. SCE)


Superpixels

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Methods

Classification

Binary Classification Problem Definition The Classification Objective The classification of each superpixel S with its correct class label Θ ∈ {θ1 , θ2 } given a vector of traversability cues X = hX1 , X2 , ...Xj , ...Xn i, which is a function of dissimilarity between the superpixel S and the model region M .



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Methods

Classification

Binary Classification Problem Definition The Classification Objective The classification of each superpixel S with its correct class label Θ ∈ {θ1 , θ2 } given a vector of traversability cues X = hX1 , X2 , ...Xj , ...Xn i, which is a function of dissimilarity between the superpixel S and the model region M .

Dissimilarity Metric M The G-statistic g(hS j khj )

=2

B X b=1

hS j [b] log

hS j [b] hM j [b]


(1)


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Methods

Classification

Simple Prior

(4)

0.6

P(Θ= 1|C)

(3)

0.4

1 1 − exp(−λC) Y0 1 P (Θ = 1|C) = Y1 1 + Y0 (eλC − 1)

P (C|Θ = 0) =

(2)

0.2

1 exp(−λC) Y1

0.0

P (C|Θ = 1) =

0.8

1.0

Probability of a Traversable Surface? A prior that favours superpixels closer to the robot as being traversable:

0

20

40

60

80

100

120

C



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Methods

Classification

Simple Prior

(4)

0.6

P(Θ= 1|C)

(3)

0.4

1 1 − exp(−λC) Y0 1 P (Θ = 1|C) = Y1 1 + Y0 (eλC − 1)

P (C|Θ = 0) =

(2)

0.2

1 exp(−λC) Y1

0.0

P (C|Θ = 1) =

0.8

1.0

Probability of a Traversable Surface? A prior that favours superpixels closer to the robot as being traversable:

0

20

40

60

80

100

120

C

In Practice The height of the superpixel centre point C defines the prior likelihood of finding traversable ground. Sapienza et al (Dept. SCE)


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Methods

Classification

Likelihood Functions A Truncated Exponential Mixture Model What is the probability of a dissimilarity metric value given it was grenerated from the traversable/non-traversable class? 1 P Xj |Θ = θ1 = exp − αj1 Xj (5) Z1 1 exp αj2 Xj P Xj |Θ = θ2 = Z0


(6)


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Methods

Classification

Likelihood Functions A Truncated Exponential Mixture Model What is the probability of a dissimilarity metric value given it was grenerated from the traversable/non-traversable class? 1 P Xj |Θ = θ1 = exp − αj1 Xj (5) Z1 1 exp αj2 Xj P Xj |Θ = θ2 = Z0

(6)

The Evidence Normalized traversability cue values accumulated in a histogram. Values obtained from Static Traversability dataset. Sapienza et al (Dept. SCE)


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Methods

Classification

Naive Bayes Classification Posterior Probability To which class Θ does the vector of traversability cues X belong? From Bayes’ rule: Pˆ (Θ = θl )P (X1 ...Xn |Θ = θl ) P Θ = θl |X1 ...Xn = P ˆ m P (Θ = θm )P (X1 ...Xn |Θ = θm )



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Methods

Classification


(7)

Assuming the traversability cues are conditionally independent: P (X1 ...Xn |Θ) =

n Y

P (Xj |Θ)

(8)

j=1



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Methods

Classification


(7)

Assuming the traversability cues are conditionally independent: P (X1 ...Xn |Θ) =

n Y

P (Xj |Θ)

(8)

j=1

Using the Maximum a Posteriori (MAP) decision rule: Θ ← arg max P (Θ = θl ) θl


n Y

P (Xj |Θ = θl )

(9)

j=1


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Methods

Classification

Expectation Maximization Learning Task Estimate a hypothesis hj = hαj1 , αj2 i that describe the rate parameters of the truncated exponential mixture model.



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Methods

Classification

Expectation Maximization Learning Task Estimate a hypothesis hj = hαj1 , αj2 i that describe the rate parameters of the truncated exponential mixture model. EM process E -Step: Calculate the Expected value E[X|Θ = θl ] assuming the current hypothesis h holds. M -Step: Calculate the new ML 0 0 hypothesis h0 = hαj1 , αj2 i assuming that the values for E[X|Θ = θl ] were those calculated from E -Step.



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Vision Algorithm

Algorithm



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Methods

Locomotion

Locomotion Depth Estimation Orientation and distance of obstacle regions can be calculated using trigonometric identities.



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Methods

Locomotion

Locomotion Depth Estimation Orientation and distance of obstacle regions can be calculated using trigonometric identities.


Boundary Interpretation Polar range plot is analysed and largest obstacle free areas beyond predefined distance are identified.


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Results

Play video



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Conclusion

Summary and Conclusion Design of vision system for a robot to guide itself safely in proximity of obstacles using the smallest reported safe window.



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Conclusion


The results demonstrate its competence in various indoor and outdoor environments without the need for prior training, temporal model, or adjustments to system parametrization.



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Conclusion



We modelled the feature dissimilarity distribution with a truncated exponential mixture.



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Conclusion



We modelled the feature dissimilarity distribution with a truncated exponential mixture.

Simple movement behaviour - move towards largest open space.



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Conclusion

Future Work

Learn multiple ground models; How to transition from one ground surface type to another automatically?.



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Conclusion

Future Work


Temporal model of traversable area, how does it change with robot movement?



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Conclusion

Future Work


Temporal model of traversable area, how does it change with robot movement?

Movement driven from probability of each superpixel being traversable, not from binary classification result.



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Conclusion

Questions?



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Conclusion

Katramados, I., Crumpler, S., & Breckon, T. (2009). Real-time traversable surface detection by colour space fusion and temporal analysis. In Int. Conf. Computer Vision Systems, volume 5815 (pp. 265–274). Kim, D., Oh, S., & Rehg, J. (2007). Traversability classification for UGV navigation: a comparison of patch and superpixel representations. In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (pp. 3166–3173). Lorigo, L., Brooks, R., & Grimson, W. (1997). Visually-guided obstacle avoidance in unstructured environments. In IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (pp. 373–379). Marr, D. (1982). Vision. A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman and Company, first edition. Ulrich, I. & Nourbakhsh, I. (2000). Appearance-based obstacle detection with monocular color vision. In AIII Conf. on Artificial Intelligence (pp. 866–871). Sapienza et al (Dept. SCE)


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