Abstract
We present a low cost localization system that exploits dense image information to continuously track the position of a range camera in 6DOF. This work has two main contributions: First, the localization of the camera is performed with respect to a set of keyframes selected according to a spatial criteria producing a less populated and more uniform distribution of keyframes in space. This allows us to avoid the computational overload caused by having to estimate a depthmap at the frame rate of the camera as it is common in other dense sequential methods. Second, we propose a two-stage approach to compute the current location of the camera with respect to its closest keyframe. During the first stage, our system calculates an initial relative pose estimate from a sparse set of 3D to 2D point correspondences. This estimate is then refined during the second stage using a dense image alignment. The refinement step is stated as a Non Linear Least Squares (NLQs) optimisation embedded in a coarse to fine approach that minimizes the photo-consistency error between the current image and a warped version of the image associated to the closest keyframe and its depth map.
To validate the accuracy of our system, we conducted experiments using datasets with perfectly known trajectory and with both, perfect ray-traced images and images with noise and blur. We also evaluate the accuracy of the system using datasets with RGBD images taken at different frame-rates, and the improvements in convergence due to our coarse-to-find approach. Our assessment shows that our system is able to achieve millimeter accuracy. Most of the expensive calculations are carried out by exploiting parallel computation on a GPU.
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References
Baker, S., Matthews, I.: Lucas-Kanade 20 years on: a unifying framework: Part 1. Int. J. Comput. Vis. (IJCV) 3(56), 221–255 (2004)
Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3D reconstruction using signed distance functions. In: Robotics: Science and Systems Conference (RSS), June 2013
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 1(40), 120–145 (2011)
Civera, J., Davison, A.J., Montiel, J.M.M.: Inverse depth parametrization for monocular SLAM. IEEE Trans. Robot. 24(5), 932–945 (2008)
Davison, A.J.: Real-time simultaneous localisation and mapping with a single camera. In: Proceedings of the International Conference on Computer Vision, Nice, October 2003
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: MonoSLAM: real-time single camera SLAM. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10605-2_54
Engel, J., Sturm, J., Cremers, D.: Semi-dense visual odometry for a monocular camera. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1449–1456, December 2013
Handa, A., Newcombe, R.A., Angeli, A., Davison, A.J.: Real-time camera tracking: when is high frame-rate best? In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7578, pp. 222–235. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33786-4_17
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004). ISBN 0521540518
Klein, G., Murray, D.W.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of the Sixth IEEE and ACM International Symposium on Mixed and Augmented Reality, November 2007
Klein, G., Murray, D.: Improving the agility of keyframe-based SLAM. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 802–815. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88688-4_59
Lovegrove, S., Davison, A.J.: Real-time spherical mosaicing using whole image alignment. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 73–86. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15558-1_6
Newcombe, R.A., Davison, A.J.: Live dense reconstruction with a single moving camera. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1498–1505, June 2010. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5539794
Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality, pp. 127–136, October 2011. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6162880
Newcombe, R.A., Lovegrove, S.J., Davison, A.J.: DTAM: dense tracking and mapping in real-time. Department of Computing, Imperial College London, UK (2012)
Piniés, P., Paz, L.M., Gálvez-López, D., Tardós, J.D.: CI-graph simultaneous localization and mapping for three-dimensional reconstruction of large and complex environments using a multicamera system. J. Field Robot. 27(5), 561–586 (2010)
Silveira, G., Malis, E., Rives, P.: An efficient direct method for improving visual SLAM, 10–14 April 2007
Steinbruker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense RGB-D images. In: Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pp. 719–722 (2011). doi:10.1109/ICCVW.2011.6130321
Stewénius, H., Engels, C., Nistér, D.: Recent developments on direct relative orientation. ISPRS J. Photogramm. Remote Sens. 60, 284–294 (2006)
Stühmer, J., Gumhold, S., Cremers, D.: Real-time dense geometry from a handheld camera. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DAGM 2010. LNCS, vol. 6376, pp. 11–20. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15986-2_2
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: Proceedings of the International Conference on Intelligent Robot Systems (IROS), October 2012
Varadarajan, V.S.: Lie Groups, Lie Algebras, and Their Representations. Graduate Text in Mathematics, vol. 102. Prentice-Hall, Englewood Cliffs (1974)
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The authors would like to thank Fundanción CEIBA for the financial support that has made the development of this work possible.
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Díaz, A., Paz, L., Caicedo, E., Piniés, P. (2016). Dense Tracking with Range Cameras Using Key Frames. In: Santos Osório, F., Sales Gonçalves, R. (eds) Robotics. SBR LARS 2016 2016. Communications in Computer and Information Science, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-47247-8_2
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