image-based segmentation of indoor corridor floors for a mobile robot yinxiao li and stanley t....
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![Page 1: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/1.jpg)
Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot
Yinxiao Li and Stanley T. BirchfieldDepartment of Electrical and Computer Engineering
Clemson University{ yinxial, stb }@clemson.edu
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MotivationGoal: Segment the floor in a single corridor image
Why?• obstacle avoidance• mapping• autonomous exploration and navigation
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MotivationGoal: Segment the floor in a single corridor image
Why?• obstacle avoidance• mapping• autonomous exploration and navigation
![Page 4: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/4.jpg)
Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
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Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
![Page 6: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/6.jpg)
Previous WorkFree space detection
• Combination of color and histogram (Lorigo 1997)
• Optical flow (Stoffler 2000, Santos-Victor 1995)
• Stereo matching (Sabe 2004)
Floor detection• Apply planar homographies to
optical flow vectors (Kim 2009, Zhou 2006)
• Stereo homographies (Fazl-Ersi 2009)
• Geometric reasoning (Lee 2009)
Limitations• Multiple images for motion• Multiple cameras for stereo• Require different colors for floor and wall• Computational efficiency• Calibrated cameras • Assume ceiling visible
Our contribution: Segment the floor in real time using a single image captured by a low-height mobile robot
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Challenging problem: Reflections
Also:• variety of poses (vanishing point, ceiling not always visible)• sometimes wall and floor color are nearly the same
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Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
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Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
David Douglas & Thomas Peucker, "Algorithms for the reduction of the number of points required to represent a digitized line or its caricature", The Canadian Cartographer 10(2), 112–122 (1973)
![Page 10: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/10.jpg)
Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
![Page 11: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/11.jpg)
Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
![Page 12: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/12.jpg)
Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
![Page 13: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/13.jpg)
Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
![Page 14: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/14.jpg)
Douglas-Peucker Algorithm
Purpose: Reduce the number of points in a curve (polyline)
Algorithm:
1. Connect farthest endpoints2. Repeat
1. Find maximum distance between the original curve and the simplified curve
2. Split curve at this point Until max distance is less than
threshold
![Page 15: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/15.jpg)
Modified Douglas-Peucker Algorithm
original algorithm: dallowed is constantmodified algorithm: dallowed is given by half-sigmoid function
dallowed
original modified
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Detecting Line Segments (LS)
![Page 17: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/17.jpg)
Detecting Line Segments (LS)
1. Compute Canny edges
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Detecting Line Segments (LS)
1. Compute Canny edges
2. Modified Douglas-Peucker algorithm to detect line segments
Vertical LS: slope within ±5° of vertical direction Horizontal LS: Slope within ±45° of horizontal direction
dallowed
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Detecting Line Segments (LS)
1. Compute Canny edges
2. Modified Douglas-Peucker algorithm to detect line segments
Vertical LS: slope within ±5° of vertical direction Horizontal LS: Slope within ±45° of horizontal direction
3. Pruning line segments (320x240) Vertical LS: minimum length 60 pixels Horizontal LS: minimum length 15 pixels Vanishing point (height selection): The vanishing point is computed as the mean of the intersection of pairs of non-vertical lines. It is used for throwing away horizontal line segments coming from windows, ceiling lights, etc.
dallowed
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Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
![Page 21: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/21.jpg)
Score Model
is assigned to each horizontal line
Structure Score Bottom Score Homogeneous Score
Weightshorizontal line
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Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1
(How to set threshold T1?)
|I(x,y)| > T1
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Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1 (gradient magnitude)
(How to set threshold T1?)
2. I(x,y) < T2 (image intensity)
(How to set threshold T2?)
|I(x,y)| > T1 and I(x,y) < T2
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Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1 (gradient magnitude)
(How to set threshold T1?)
2. I(x,y) < T2 (image intensity)
(How to set threshold T2?)
We applied SVM to 800 points in 200 images:
|I(x,y)| > T1 and I(x,y) < T2
![Page 25: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/25.jpg)
Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1 (gradient magnitude)
(How to set threshold T1?)
2. I(x,y) < T2 (image intensity)
(How to set threshold T2?)
Approximate using two thresholds:
|I(x,y)| > T1 and I(x,y) < T2
![Page 26: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/26.jpg)
Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1 (gradient magnitude)
2. I(x,y) < T2 (image intensity)
3. Now threshold original image using TLC (average gray level of pixels satisfying #1 and #2)
I(x,y) < TLC
![Page 27: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/27.jpg)
Score Model – Structure
Given a typical corridor image
1. |I(x,y)| > T1 (gradient magnitude)
2. I(x,y) < T2 (image intensity)
3. Now threshold original image using TLC (average gray level of pixels satisfying #1 and #2)
4. Compute the chamfer distance between the line segments and structure blocks
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Score Model – Structure
Ridler-Calvard Otsu
Our method
Our method is able to remove the spurious pixels on the floor caused by reflections or shadows
Method R-C Otsu Ours
Correctness 62% 66% 82%
Comparison of different threshold methods
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Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
![Page 30: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/30.jpg)
Score Model – Bottom
1. Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary
![Page 31: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/31.jpg)
Score Model – Bottom
1. Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary
![Page 32: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/32.jpg)
Score Model – Bottom
1. Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary
2. Compute the distance of each horizontal line segment to the “bottom” wall-floor boundary
(red circled horizontal line segments indicates a positive contribution)
![Page 33: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/33.jpg)
Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
![Page 34: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/34.jpg)
Score Model – HomogeneousIdea: The floor tends to have larger
regions (due to decorations, posters, windows on the wall).
Algorithm:
1. Color-based segmentation of the image (using Felzenszwalb-Huttenlocher’s minimum spanning tree algorithm)
2. For each horizontal line segment, compute size of region
just belowsegment
size of largestsegment
![Page 35: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/35.jpg)
Score Model – HomogeneousIdea: The floor tends to have larger
regions (due to decorations, posters, windows on the wall).
Algorithm:
1. Color-based segmentation of the image (using Felzenszwalb-Huttenlocher’s minimum spanning tree algorithm)
2. For each horizontal line segment, compute size of region
just belowsegment
size of largestsegment
![Page 36: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/36.jpg)
Segmenting the floor
• Normalize the scores and sum• Threshold the final score:
• Connect the remaining line segments and extend the endpoints to the edges of the image
![Page 37: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/37.jpg)
Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogenous Score
• Experimental Results
• Conclusion
![Page 38: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/38.jpg)
Evaluation Criterion• Wall- floor Boundary
– Green line: Detected wall-floor boundary
– Red line: Ground truth
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Evaluation Criterion• Wall- floor Boundary
– Green line: Detected wall-floor boundary
– Red line: Ground truth
• Area– Blue shade area: misclassified area
– Red shade area: ground truth floor area
• Error Rate
• Segmentation is considered successful if rerr < 10%
%err
bluer
red
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Sample Results89.1% success on database of 426 images:
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Sample ResultsImages downloaded from the internet:
![Page 42: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/42.jpg)
Sample ResultsSome successful results on failure examples from Lee et al. 2008
Original Image
Lee’s Ours
D. C. Lee, M. Hebert, and T. Kanade. “Geometric Reasoning for Single Image Structure Recovery”. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
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Sample Results• Failure examples
Checkered floor
Textured wall
Darkimage Bright
lights
![Page 44: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/44.jpg)
Sample Results
• Video clip 1
![Page 45: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/45.jpg)
Sample Results
• Video clip 2
![Page 46: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/46.jpg)
Outline
• Previous Work
• Detecting Line Segments
• Score Model for Evaluating Line Segments
– Structure Score
– Bottom Score
– Homogeneous Score
• Experimental Results
• Conclusion
![Page 47: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/47.jpg)
Conclusion• Summary
- Edge-based approach to segmenting floors in corridors
- Correctly handles specular reflections on floor- Nearly 90% of the corridor images in our database
can be correctly detected.- Speed: approximately 7 frames / sec
• Future work- Speed up the current algorithm- Improving score model by add more visual cues
(highly textured floors, low resolution, or dark environment)
- Use floor segmentation for mapping
![Page 48: Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot Yinxiao Li and Stanley T. Birchfield Department of Electrical and Computer Engineering](https://reader036.vdocuments.site/reader036/viewer/2022070411/56649f4f5503460f94c70ef5/html5/thumbnails/48.jpg)
Acknowledgement Clemson Computer Vision Group reviewers
• Zhichao Chen• Vidya Murali
Anonymous reviewers
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Thanks!
Questions?
Image-Based Segmentation of Indoor Corridor Floors for a Mobile Robot