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EUCLIDEAN POSITION ESTIMATION OF FEATURES ON A STATIC OBJECT USING A MOVING CALIBRATED CAMERA Nitendra Nath, David Braganza ‡, and Darren Dawson EUCLIDEAN POSITION ESTIMATION OF FEATURES ON A STATIC OBJECT USING A MOVING CALIBRATED CAMERA Nitendra Nath, David Braganza ‡, and Darren Dawson Department of Electrical and Computer Engineering, Clemson University, Clemson, SC , Abstract What is Euclidean Position Estimation? Three-dimensional (3D) reconstruction of an object, where the Euclidean coordinates of feature points on a moving or fixed object are recovered from a sequence of two- dimensional (2D) images is known as Euclidean position estimation or more broadly known as Structure from Motion (SFM) or Simultaneous Localization and Mapping (SLAM). They have significant impact on several applications such as: A 3D Euclidean position estimator using a single moving calibrated camera whose position is assumed to be measurable is developed in this paper to asymptotically recover the structure of a static object. To estimate the structure, an adaptive least squares estimation strategy is employed based on a novel prediction error formulation and a Lyapunov stability analysis. Autonomous vehicle navigationPath planningSurveillance Geometric Model A geometric relationship is developed between a moving camera and a stationary object. n feature points located on a static object, denoted by are considered. ¹ m i, [ x i y i z i ] T F i 8 i = 1 ;:::; n 3D coordinates of i th feature point w.r.t. : C Normalized Euclidean coordinates : m i, 1 z i ¹ m i = [ x i = z i y i = z i 1 ] T Corresponding projected pixel coordinates : p i, [ u i v i ] T, u i ( t ) 2 R v i ( t ) 2 R Pin-hole camera model: p i = A m i = 1 z i A ¹ m i A, · fk u fk u co t Á u 0 0 fk v s i n Á v 0 ¸ : Known constant intrinsic calibration matrix of the camera A 2 R 2 £ 3 The objective of this work is to accurately identify the unknown constant Euclidean coordinates of the feature x fi relative to the world frame in order to recover the 3D structure of the object. Geometric relationships between the fixed object, mechanical system and the camera. R b ( t ) 2 SO ( 3 ) x b ( t ) 2 R 3 R c ( t ) 2 SO ( 3 ) x c ( t ) 2 R 3 Measurable rotation matrix and translation vector from B to W Known constant rotation matrix and translation vector from C to B x f i 2 R 3 ¹ m i ( t ) 2 R 3 Unkown Euclidean Structure Estimation From the geometric model, the following expression can be obtained: ¹ m i = R T c £ R T b ( x f i ¡ x b ) ¡ x c ¤ After utilizing pin-hole camera model, pixel coordinates of i th feature point can be written as: p i = 1 z i AR T c £ R T b ( x f i ¡ x b ) ¡ x c ¤ Corresponding depth z i = R T c 3 £ R T b ( x f i ¡ x b ) ¡ x c ¤ Last row of R T c ( t ) in parameterized form: p i ( t ) p i = 1 ¦£ i W£ i W£ i = AR T c £ R T b ( x f i ¡ x b ) ¡ x c ¤ ¦£ i = z i = R T c 3 £ R T b ( x f i ¡ x b ) ¡ x c ¤, Prediction error for i th feature point ~ p i = 1 ¦£ i ( W ¡ ^ p i ¦ ) ~ £ i Combined prediction error ~ p = B ¹ W p ~ £ ¦ ( t ) 2 R 1 £ 4, W ( t ) 2 R 2 £ 4 : measurable regression matrices £ i 2 R 4 : unkown constant parameter vector ¹ W p ( t ) 2 R 2 n £ 4 n B ( t ) 2 R 2 n £ 2 n ~ £ ( t ) 2 R 4 n : measurable signal : auxiliary matrix : combined estimation error Adaptive update law is designed as: : ^ £, P ro j © ® ¡ ¹ W T p ~ p ª P ro j f ¢ g ® ( t ) 2 R ¡ ( t ) 2 R 4 n £ 4 n : ensures positiveness of the term ¦ ( t ) ^ £ i ( t ) : a positive scalar function : least-squares estimation gain matrix Simulation Results Case 1: No noise added to pixel coordinates Distance Estimation Error ObjectActual distance (cm)Estimated distance (cm)Error (cm)Convergence time (sec) Case 1 Length I Length II Length III Case 2 Length I Length II Length III Case 3 Length I Length II Length III Experimental Results Case 2: Gaussian noise of variance 200 added to pixel coordinates Case 3: Gaussian noise of variance 400 added to pixel coordinates Robot Control PCVision PC 15 Hz Trigger PUMA 560 Robot Object Monochrome CCD Camera Experimental testbed with camera, robot and object ObjectActual distance (cm) Estimated distance (cm) Error (cm) Convergence time (sec) Length I Length II Length III Length IV Length V Length VI Object I: Checker-boardDistance Estimation Error Object II: Doll-houseDistance Estimation Error ObjectActual distance (cm) Estimated distance (cm) Error (cm) Convergence time (sec) Length I Length II Length III Length IV Length V Length VI Object III: Tool-boxesDistance Estimation Error ObjectActual distance (cm) Estimated distance (cm) Error (cm) Convergence time (sec) Length I Length II Length III Length IV Length V Length VI The estimator accurately identifies the Euclidean distances between the features without having any information with regard to the object’s geometry. ‡ D. Braganza is with OFS, 50 Hall Road, Sturbridge, MA KLT feature tracking algorithm was used for tracking feature points from one frame to another.
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