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| Method of Improving Practicability of Indoor Visual Odometry |
| PENG Tianbo1, WANG Hengsheng1,2, ZENG Bin1 |
1.College of Mechanical and Electrical Engineering, Central South University, Changsha 410083
2.State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083 |
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Abstract Aiming at the controversy of the real-time performance, robustness and accuracy in visual odometry, method of improving practicability of indoor visual odometry is put forward to tackle the problem. The corner features of every image in the sequence are obtained using graphics processing unit based oriented FAST and rotated BRIE algorithm and matched using K Nearest neighbor algorithm to reduce the computation time. According to the measurement range of Kinect, points with high measurement error are rejected. To solve the movement of the camera between two frames, the estimation of movement parameters are firstly obtained with efficient perspective-n-point algorithm. Then, they are used as the initial value of Levenberg-Marquedt algorithm to refine the parameters. Random sample consensus is used to reject outliers during the computation of the camera movement. The experimental results show that the proposed method is effective for the accuracy improvement of the motion trajectory calculation.
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Received: 01 November 2016
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| Fund:Supported by National Basic Research Program of China(No.2013CB035504), Fundamental Research Funds for the Central Universities of Central South University (No.2016zzts313) |
About author:: PENG Tianbo, born in 1992, master student. His research interests include machine vision and intelligent robot.
WANG Hengsheng(Corresponding author), born in 1963, Ph.D., professor. His research interests include intelligent robot.
ZENG Bin, born in 1993, master student. His research interests include machine vision. |
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[1] SCARAMUZZA D, FRAUNDORFER F. Visual Odometry: Part I: The First 30 Years and Fundamentals. IEEE Robotics & Automation Magazine, 2011, 18(4): 80-92.
[2] WARREN M, CORKE P, UPCROFT B. Long-Range Stereo Visual Odometry for Extended Altitude Flight of Unmanned Aerial Vehicles. International Journal of Robotics Research, 2016, 35(4): 381-403.
[3] NISTER D, NARODITSKY O, BERGEN J. Visual Odometry // Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2004, I: 652-659.
[4] BELLAVIA F, FANFANI M, PAZZAGLIA F, et al. Robust Selective Stereo SLAM without Loop Closure and Bundle Adjustment // Proc of the 17th International Conference on Image Analysis and Processing. Heidelberg, Germany: Springer, 2013: 462-471.
[5] MORAVEC H P. Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Ph.D Dissertation. Stanford, USA: Stanford University, 1980.
[6] HARRIS C G, PIKE J M. 3D Positional Integration from Image Sequences. Image & Vision Computing, 1988, 6(2): 87-90.
[7] ROSTEN E, DRUMMOND T. Machine Learning for High-Speed Corner Detection // Proc of the 9th European Conference on Computer Vision. Heidelberg, Germany: Springer, 2006: 430-443.
[8] LOWE D G. Distinctive Image Features from Scale-Invariant Key-points. International Journal of Computer Vision, 2004, 60(2): 91-110.
[9] BAY H, ESS A, TUYTELAARS T, et al. Speeded up Robust Features(SURF). Computer Vision and Image Understanding, 2008, 110(3): 346-359.
[10] AGRAWAL M, KONOLIGE K, BLAS M R. CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching // Proc of the 10th European Conference on Computer Vision. Heidelberg, Germany: Springer, 2008, IV: 102-115.
[11] 郑 驰,项志宇,刘济林.融合光流与特征点匹配的单目视觉里程计.浙江大学学报(工学版), 2014, 48(2): 279-284.
(ZHENG C, XIANG Z Y, LIU J L. Monocular Vision Odometry Based on the Fusion of Optical Flow and Feature Points Matching. Journal of Zhejiang University(Engineering Science), 2014, 48(2): 279-284.)
[12] AZARTASH H, BANAI N, NGUYEN T Q. An Integrated Stereo Visual Odometry for Robotic Navigation. Robotics and Autonomous Systems, 2014, 62(4): 414-421.
[13] CALONDER M, LEPETIT V, ZUYSAL M, et al. BRIEF: Computing a Local Binary Descriptor Very Fast. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1281-1298.
[14] LEUTENEGGER S, CHLI M, SIEGWART R Y. BRISK: Binary Robust Invariant Scalable Keypoints // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2011: 2548-2555.
[15] SAHA S, D MOULIN V. ALOHA: An Efficient Binary Descriptor Based on Haar Features // Proc of the 19th IEEE International Conference on Image Processing. Washington, USA: IEEE, 2012: 2345-2348.
[16] SCHMIDT A, KRAFT M. The Impact of the Image Feature Detector and Descriptor Choice on Visual SLAM Accuracy // CHORAS R S . Image Processing & Communications Challenges 6. Berlin, Germany: Springer, 2015: 203-210.
[17] RUBLEE E, RABAUD V, KONOLIGE K, et al. ORB: An Efficient Alternative to SIFT or SURF // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2011: 2564-2571.
[18] 钟华民,王 伟,张慧华.结合ORB特征和色彩模型的视觉跟踪算法.模式识别与人工智能, 2015, 28(1): 90-96.
(ZHONG H M, WANG W, ZHANG H H. Visual Tracking Algorithm Combining ORB Feature and Color Model. Pattern Recognition and Artificial Intelligence, 2015, 28(1): 90-96.)
[19] FRAUNDORFER F, SCARAMUZZA D. Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications. IEEE Robotics & Automation Magazine, 2012, 19(2): 78-90.
[20] LEPETIT V, MORENO-NOGUER F, FUA P. EPnP: An Accurate O(n) Solution to the PnP Problem. International Journal of Computer Vision, 2009, 81(2): 155-166.
[21] NISTE R D. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(6): 756-777. |
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2017, Vol. 30 
