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Segmentation of Floor in Corridor Images for Mobile Robot Navigation

This project aims to segment the floor in corridor images for mobile robot navigation, with the goal of obstacle avoidance, mapping, and autonomous exploration and navigation. The approach involves detecting line segments and evaluating their scores based on structure and homogeneity. The proposed method is also capable of dealing with challenging factors such as reflections and varying poses. The Douglas-Peucker algorithm and Canny edge detection are used for line segment detection. Experimental results and future extensions are discussed.

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Segmentation of Floor in Corridor Images for Mobile Robot Navigation

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  1. Segmentation of Floor in Corridor Images for Mobile Robot Navigation Yinxiao Li Clemson University Committee Members: Dr. Stanley Birchfield (Chair) Dr. Adam Hoover Dr. John Gowdy

  2. Motivation Goal: Segment the floor in a single corridor image • Why? • obstacle avoidance • mapping • autonomous exploration and navigation

  3. Motivation Goal: Segment the floor in a single corridor image • Why? • obstacle avoidance • mapping • autonomous exploration and navigation

  4. Outline • Previous Work • Detecting Line Segments • Score Model for Evaluating Line Segments • Structure Score • Bottom Score • Homogeneous Score • Experimental Results • Extension

  5. Outline • Previous Work • Detecting Line Segments • Score Model for Evaluating Line Segments • Structure Score • Bottom Score • Homogeneous Score • Experimental Results • Extension

  6. Previous Work Free space detection Floor detection Apply planar homographies to optical flow vectors (Kim 2009, Zhou 2006) Stereo homographies (Fazl-Ersi 2009) Geometric reasoning (Lee 2009) • Combination of color and histogram (Lorigo 1997) • Optical flow (Stoffler 2000, Santos-Victor 1995) • Stereo matching (Sabe 2004) • 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

  7. Challenging problem: Reflections • Also: • variety of poses (vanishing point, ceiling not always visible) • sometimes wall and floor color are nearly the same

  8. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • 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)

  9. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • Split curve at this point Until max distance is less than threshold

  10. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • Split curve at this point Until max distance is less than threshold

  11. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • Split curve at this point Until max distance is less than threshold

  12. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • Split curve at this point Until max distance is less than threshold

  13. Douglas-Peucker Algorithm • Purpose: Reduce the number of points in a curve (polyline) Algorithm: • Connect farthest endpoints • Repeat • Find maximum distance between the original curve and the simplified curve • Split curve at this point Until max distance is less than threshold

  14. Detecting Line Segments (LS)

  15. Detecting Line Segments (LS) Compute Canny edges

  16. Detecting Line Segments (LS) • Compute Canny edges • Douglas-Peucker algorithm to detect line segments • Vertical LS: slope within ±5° of vertical direction • Horizontal LS: Slope within ±45° of horizontal direction

  17. Detecting Line Segments (LS) • Compute Canny edges • 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.

  18. Outline • Previous Work • Detecting Line Segments • Score Model for Evaluating Line Segments • Structure Score • Bottom Score • Homogeneous Score • Experimental Results • Extension

  19. Score Model Weights horizontal line Bottom Score Structure Score Homogeneous Score • is assigned to each horizontal line

  20. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude)(How to set threshold T1?) |I(x,y)| > T1

  21. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude)(How to set threshold T1?) I(x,y) < T2(image intensity) (How to set threshold T2?) |I(x,y)| > T1 and I(x,y) < T2

  22. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude)(How to set threshold T1?) 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

  23. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude)(How to set threshold T1?) I(x,y) < T2(image intensity) (How to set threshold T2?) Approximate using two thresholds: T1 |I(x,y)| > T1 and I(x,y) < T2 T2

  24. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude) I(x,y) < T2(image intensity) Now threshold original image using TLC (average gray level of pixels satisfying #1 and #2) I(x,y) < TLC

  25. Score Model – Structure Given a typical corridor image |I(x,y)| > T1(gradient magnitude) I(x,y) < T2(image intensity) Now threshold original image using TLC (average gray level of pixels satisfying #1 and #2) Compute the chamfer distance between the line segments and structure blocks

  26. Score Model – Structure Comparison of different threshold methods Ridler-Calvard Otsu Our method is able to remove the spurious pixels on the floor caused by reflections or shadows Our method

  27. Score Model – Bottom Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary

  28. Score Model – Bottom Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary

  29. Score Model – Bottom Connect the bottom points of consecutive vertical line segments to create “bottom” wall-floor boundary Compute the distance of each horizontal line segment to the “bottom” wall-floor boundary (red circled horizontal line segments are more likely on the real wall-floor boundary)

  30. Score Model – Homogeneous Idea: The floor tends to have larger regions (due to decorations, posters, windows on the wall). Algorithm: Color-based segmentation of the image (using Felzenszwalb-Huttenlocher’s minimum spanning tree algorithm) For each horizontal line segment, compute size of region just below segment size of largest segment

  31. Score Model – Homogeneous Idea: The floor tends to have larger regions (due to decorations, posters, windows on the wall). Algorithm: Color-based segmentation of the image (using Felzenszwalb-Huttenlocher’s minimum spanning tree algorithm) For each horizontal line segment, compute size of region just below segment size of largest segment

  32. 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

  33. Evaluation Criterion • Wall- floor Boundary • Green line: Detected wall-floor boundary • Red line: Ground truth

  34. 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%

  35. Outline • Previous Work • Detecting Line Segments • Score Model for Evaluating Line Segments • Structure Score • Bottom Score • Homogeneous Score • Experimental Results • Extension

  36. Sample Results 89.1% success on database of 426 images:

  37. Sample Results Images downloaded from the internet:

  38. Sample Results Lee’s Original Image Ours Some successful results on failure examples from Lee et al. 2009 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.

  39. Sample Results Textured wall Checkered floor Dark image Bright lights Failure examples

  40. Sample Videos Video clip 1

  41. Sample Videos Video clip 2

  42. Outline • Previous Work • Detecting Line Segments • Score Model for Evaluating Line Segments • Structure Score • Bottom Score • Homogeneous Score • Experimental Results • Extension

  43. Floor Segmentation Algorithm on Low-Res Images • Apply floor-segmentation algorithm to varying image resolutions • Simply change one parameter: Minimum length of a horizontal line segment: • Combine with Corridor Orientation line estimation* * V. N. Murali and S. T. Birchfield, “Autonomous Exploration Using Rapid Perception of Low-Resolution Image Information”, Autonomous Robots (In review)

  44. Results for varying resolutions • Wall-floor Boundary

  45. Results for minimalistic geometry • Minimalistic geometry estimation* *V. Murali, Y. Li, and S.T. Birchfield, “Extracting Minimalistic Corridor Geometry from Low-Resolution Images”, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.(In review)

  46. Sample Videos 1

  47. Sample Videos 2

  48. Conclusion • Image-Based Floor Segmentation • 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 • Extension • Three line “geometry”---enough for basic robot navigation

  49. Future Work • Image-Based Floor Segmentation • Speed up the current high-res algorithm • Improving score model by add more visual cues(highly textured floors, low resolution, or dark environment) • Adapt weights by Adaboost training • Use floor segmentation for mapping

  50. Thanks! Questions? Segmentation of Floor in Corridor Images for Mobile Robot Navigation ---By Yinxiao Li

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