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

Robot Vision Lab. 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

Robot Vision Lab. 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.

Robot Vision Lab. 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

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

Robot Vision Lab. 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 U Local maximum Threshold value

Robot Vision Lab. 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)

Robot Vision Lab. 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. Fig. 3 Floor contacted lines and visible regions. Contacted line : x 1, x 2, x 3. Non-contacted line : x 4. Visible region : l 1, l 2, r 2, l 3, r 3, r 4. Occluded region : r 1, l 4

Robot Vision Lab. 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

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

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

Robot Vision Lab. 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). (5) (6) (4)

Robot Vision Lab. 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)

Robot Vision Lab. 3. Experimental results (1) Real position (mm, °)Measured position (mm, °) No.XYAngleXY Real position (mm, °)Measured position (mm, °) No.XYAngleXY  of errors Table 1 Real positions and errors

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

Robot Vision Lab. 3. Experimental results (3) Fig. 8. Input image of each sequence

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

Robot Vision Lab. 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.