Localization for Mobile Robot Using Monocular Vision

1. Localization for Mobile Robot Using Monocular Vision Hyunsik Ahn Jan. 2006 Tongmyong University

2. 1. Introduction (1) • Self-localization methods of mobile robot • Position tracking : encoder, ultrasonic sensors, local sensors • Global localization : laser-range scanner, vision-based methods • Vision-based methods of indoor application • Stereo vision • Directly detects the geometric information, complicated H/W, much processing time • Omni-directional view • Using conic mirror, low resolution • Mono view using landmarks • Using artificial landmarks

3. 1. Introduction (2) • Related work in monocular method • Sugihara(1988) did pioneering works in localization using vertical edges. • Atiya and Hager(1993) used geometric tolerance to describe observation error. • Kosaka and Kak (1992) proposed a model-based monocular vision system with a 3D geometric model. • Munoz and Gonzalez (1998) added an optimization procedure. • Talluri and Aggarwal (1996) considered correspondence problem between a stored 3D model and 2D image in an outdoor urban environment. • Aider et. al. (2005) proposed an incremental model-based localization using view-invariant regions. • Another approach adopting SIFT (Scale-Invariant Feature Transformation) algorithm to comput correspondence between the SIFT features saved and images during navigation.

4. 1. Introduction (3) • A self-localization method using vertical lines with mono view is proposed. • Indoor environment, use horizontal and vertical line features(doors, furniture) • Find vertical lines, compute pattern vectors • Match the lines with the corners of map • Find position (x,y,θ) with matched information

5. Detect line segments Input image 2. Localization algorithm Map-making and path planning No Line segments ≥ 3 Yes Matching lines with map Localization(x,y,θ) No Uncertainty > T Yes No Destination Yes end Fig. 1 The flowchart of self-localization

6. Local maximum Threshold value U 2.1 Line feature detection • Vertical Sobel operation • Vertically projected histogram • One dimensional averaging, and thresholding • Local maximum are indexed as feature points Fig. 2 Projected histogram and a local maximum

7. 2.2 Correspondence of feature vectors (1) • Using geometrical information of the line features of the map • Feature vectors are defined with hue(H) and saturation(S) • Feature vectors of the right and left regions are defined • Check whether a line meats floor regions • Contacted line, non-contacted line : • Define visibility of regions of contacted line • Visible region, Occluded region (1)

8. 2.2 Correspondence using feature vectors (2) • Matching of feature vector of lines with map. • Lines of both visible region, one visible region, non-contacted line • The correspondence of neighbor lines are investigated with the lines having geometrical relationship. . Contacted line : x1 ,x2 ,x3 . Non-contacted line : x 4 . Visible region : l1, l2, r2, l3, r3, r4 . Occluded region : r1 , l4 Fig. 3 Floor contacted lines and visible regions

9. 2.3 Self-localization using vertical lines (1) • The coordinates of feature points are matched to the camera coordinates of the map . Fig. 4 Global and camera coordinates

10. 2.3 Self-localization using vertical lines(2) :Camera coordinates : Feature points of camera coordinates :Features of image plane : Focal length of camera : Image plane coordinates Fig. 5 Perspective transformation of camera coordinates

11. 2.3 Self-localization using vertical lines(3) • Camera coordinates can be transformed to world coordinates by a rigid body transformation T. (2) • The camera coordinates and world coordinates are related with translation and rotation. The transformation T can be defined as (3)

12. 2.3 Self-localization using vertical lines(4) • Global coordinates are mapped to camera coordinates. • The perspective transformation is (5) • Perspective transformation and rigid transformation of the coordinates induce a system of nonlinear equations. • induces from (4), (5). (4) (5) (6)

13. 2.3 Self-localization using vertical lines(5) • Jacobian matrix • Newton’s method to find the solution of the nonlinear equations is (8) when initial value is given. where (7) (8)

14. 3. Experimental results(1) Table 1 Real positions and errors

15. 3. Experimental results(2) (a) Original Image (b) Vertical edges Fig. 6 Mobile robot (c) Projected histogram (d) Vertical lines Fig. 7 The procedures of detecting vertical lines

16. 3. Experimental results(3) Fig. 8. Input image of each sequence

17. 3. Experimental results(4) Fig. 9. The result of localization in the given map Fig. 10. Errors through Y axis

18. 4. Conclusions • A self-localization method using vertical line segment with mono view was proposed. • Line features are detected by projected histogram of edge image. • Pattern vectors and their geometrical properties are used for match with the point of map. • A system of nonlinear equations with perspective and rigid transformation of the matched points is induced. • Newton’s method was used to solve the equations. • The proposed algorithm using mono view is simple and applicable to indoor environment.