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Some Remarks on the Optimization-Based Trajectory Reconstruction of an RGB-D Sensor

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Image Processing and Communications Challenges 7

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 389))


In this paper we present an analysis of the optimization-based trajectory reconstruction of an RGB-D sensor. Several approaches varying in the error function formulation as well as the camera’s poses and features’ positions initialization are considered. Their performance in terms of both the accuracy and the processing time is evaluated within a simulated environment.

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  1. Davison, A., Reid, I., Molton, N., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)

    Article  Google Scholar 

  2. Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3D mapping with an RGB-D camera. IEEE Trans. Robot. (T-RO) 30(1), 177–187 (2013)

    Google Scholar 

  3. Grisetti, G., Kümmerle, R., Stachniss, C., Burgard, W.: A tutorial on graph-based SLAM. IEEE Intell. Trans. Syst. Mag. 2(4), 31–43 (2010)

    Google Scholar 

  4. Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2003)

    Google Scholar 

  5. Kaess, M., Dellaert, F.: Probabilistic structure matching for visual SLAM with a multi-camera rig. Comput. Vis. Image Underst. 114(2), 286–296 (2010), special issue on Omnidirectional Vision, Camera Networks and Non-conventional Cameras

    Google Scholar 

  6. Khoshelham, K., Elberink, S.O.: Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors 12(2), 1437–1454 (2012)

    Article  Google Scholar 

  7. Lachat, E., Macher, H., Mittet, M.A., Landes, T., Grussenmeyer, P.: First experiences with kinect v2 sensor for close range 3D modelling. ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (2015)

    Google Scholar 

  8. Lourakis, M.I., Argyros, A.A.: SBA: a software package for generic sparse bundle adjustment. ACM Trans. Math. Softw. (TOMS) 36(1), 2 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  9. Lu, F., Milios, E.: Globally consistent range scan alignment for environment mapping. Auton. Robots 4(4), 333–349 (1997)

    Article  Google Scholar 

  10. Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: Eighteenth National Conference on Artificial Intelligence. pp. 593–598. American Association for Artificial Intelligence, Menlo Park, CA, USA (2002)

    Google Scholar 

  11. Nowicki, M., Skrzypczyński, P.: Combining photometric and depth data for lightweight and robust visual odometry. In: 2013 European Conference on Mobile Robots (ECMR), pp. 125–130 (Sept 2013)

    Google Scholar 

  12. Park, J.H., Shin, Y.D., Bae, J.H., Baeg, M.H.: Spatial uncertainty model for visual features using a KinectTM sensor. Sensors 12(7), 8640–8662 (2012)

    Article  Google Scholar 

  13. Schmidt, A., Fularz, M., Kraft, M., Kasiński, A., Nowicki, M.: An indoor RGB-D dataset for the evaluation of robot navigation algorithms. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) Advanced Concepts for Intelligent Vision Systems, Lecture Notes in Computer Science, vol. 8192, pp. 321–329. Springer International Publishing (2013)

    Google Scholar 

  14. Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D slam systems. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 573–580. IEEE (2012)

    Google Scholar 

  15. Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: Vision algorithms: theory and practice, pp. 298–372. Springer (2000)

    Google Scholar 

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This research was financed by the Polish National Science Centre grant funded according to the decision DEC-2013/09/B/ST7/01583, which is gratefully acknowledged.

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Correspondence to Adam Schmidt .

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Schmidt, A., Kraft, M., Belter, D., Kasiński, A. (2016). Some Remarks on the Optimization-Based Trajectory Reconstruction of an RGB-D Sensor. In: Choraś, R. (eds) Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol 389. Springer, Cham.

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  • Print ISBN: 978-3-319-23813-5

  • Online ISBN: 978-3-319-23814-2

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