2010 IEEK Fall Conference - Table of Contents - KAIST

2010년 대한전자공학회 추계학술대회 논문집로봇 자율주행을 위한 강인한 실시간쌍안 카메라 6차원 자세 추정 알고리즘*강정원, 정명진한국과학기술원 전기및전자공학과e-mail : kctown@rr.kaist.ac.kr, mjchung@ee.kaist.ac.krRobust Real-Time Stereo Camera6D Pose Estimation for Robot Navigation*Jungwon Kang, Myung Jin ChungDepartment of Electrical EngineeringKAISTAbstractThis paper introduces a robust real-time stereocamera pose estimation algorithm. It is afeature-based pose estimation method with outlierremoval scheme and pose refinement usingnon-linear optimization. The proposed method isverified through an experiment with a 250mtrajectory in a real environment.I. Introd uctionPose estimation of a robot is the essentialcapability for autonomous navigation [1][2]. Inthis paper, we introduce a robust real-time stereocamera pose estimation method.II. The Proposed MethodThe Harris corner [3] is used as a feature.Motion correspondence is found by the KLT tracker[4]. Stereo correspondence is found, usingZ NCC measure [5 ]. After solving the correspond enceproblems, motion hypotheses are generated by the 3point algorithm [6]. In order to evaluate each motionhypothesis, a constraint that relates the motionhypothesis to the measurement of features wasderived. We proposed a new constraint as in Eq.(1), inspired by [7].z' H sz(1)HereH B H B −C1' 1s=C1, where z is the measurementbefore motion, z ' is the one after motion,point in 3D space,C1 X is ajKi is the jth row vector of ithC2camera intrinsic matrix, and HC1 is a homogeneoustransformation including geometric relations of stereocamera.1⎡ K10 ⎤⎢⎥1 C2⎢⎡K2 0⎤HC1⎥B = ⎣ ⎦ ⎢ 2 ⎥⎢ K10 ⎥⎢ 3K10 ⎥⎣⎦[ ] T C1z = x1 x2 y 1 B X(3)The RANSAC [8 ] is used for robust and accurateestimation under the situation with erroneousmeasurement. A hypothesis with the largest number(2)

2010년 대한전자공학회 추계학술대회 논문집of inliers is selected as the best motion hypothesis.After selecting the best motion hypothesis, we refinethe motion using L evenberg-Marquard t algorithm[9].Ⅲ. Implementation ResultAcknowledgementsAuthor are gratefully acknowledging the financialsupport by Agency for Defense Development and byUTRC(Unmanned Technology Research Center),Korea Advanced Institute of Science andTechnology.ReferenceF ig. 2 . Ex periment environmentFig. 3. Experiment resultThe ex periment environment is shown in Fig. 2 .We gathered stereo images using a Bumblebee TMstereo camera, following an marked 2 5 0 m path inFig. 2 . The estimated path with a pair of stereoimage is drawn in Fig. 3. The algorithm operated at7~ 10 fps. As the proposed method estimates themotion between only two frames and augments themotion, it inevitably accumulates the error as theprocessed frames increase.Ⅳ. ConclusionWe proposed a robust real-time stereo camerapose estimation algorithm. It is a feature-based poseestimation method with outlier removal scheme andpose refinement using non-linear optimization. Theproposed method is verified through a realex periment.[1] I. Skog, P. H andel, “In-Car Positioning andNavigation Technologies - A Survey,” IEEETransactions on Intelligent TransportationSystems, Vol. 10, No. 1, pp. 4 - 21, March 2009.[2] D. Nister, O. Naroditsky and J. Bergen, “VisualOdometry for Ground V ehicle Applications,”Journal of Field Robotics, Vol. 23, No. 1, pp. 3 -20, January 2006.[3] C. Harris and M. Stephens, “A combined cornerand ed ge detector,” Proceedings of The FourthAlvey Vision Conference, pp. 147 - 151, 1988.[4] B. D. L ucas and T. Kanade, “An Iterative ImageRegistration Technique with an Application toStereo Vision,” International Joint Conference onArtificial Intelligence, pp 674 - 679, 1981.[5] W . van der Mark and D. M. Gavrila,“Real-Time Dense Stereo for IntelligentVehicles,” IEEE Transactions on IntelligentTransportation Systems, Vol. 7, No. 1, March2006.[6] R. Haralick, C. Lee, K. Ottenberg and M. Nolle,“Review and Analysis of Solutions of the ThreePoint Perspective Pose Estimation Problem,”International Journal of Computer V ision,13(3):331-356, 1994.[7 ] D. Demirdjian and T. Darrell, “ Motion estimationfrom disparity images,” In Proc. InternationalConference on Computer V ision, volume 1 , pages213–218, July 2001.[8] M. Fischler and R. Bolles, “Random sampleconsensus: A paradigm for model fitting withapplications to image analysis and automatedcartography,” Communications of the ACM , Vol.24, No. 6, pp. 381 - 395, June 1981.[9 ] M. I.A. Lourakis, “levmar: L evenberg-marquard tnonlinear least squares algorithms in c/c++ ,”http:/ / www.ics. forth. gr/ ~ lourakis/levmar/ .