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Spectral registration of noisy sonar data for underwater 3D mapping

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Abstract

3D mapping is very challenging in the underwater domain, especially due to the lack of high resolution, low noise sensors. A new spectral registration method is presented that can determine the spatial 6 DOF transformation between pairs of very noisy 3D scans with only partial overlap. The approach is hence suited to cope with sonar as the predominant underwater sensor. The spectral registration method is based on Phase Only Matched Filtering (POMF) on non-trivially resampled spectra of the 3D data.

Two extensive sets of experiments are presented. First, evaluations with simulated data are done where the type and amount of noise can be controlled and the ground truth transformations between scans are known. Second, real world data from a Tritech Eclipse sonar is used. Concretely, 18 sonar scans of a large structure in form of a flood gate and a lock in the river Lesum in Bremen are used for 3D mapping. In doing so, the spectral registration method is compared to two other methods suited for noisy 3D registrations, namely Iterative Closest Point (ICP) and plane-based registration. It is shown that the spectral registration method performs very well in terms of the resulting 3D map as well as its run-times.

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References

  • Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-d shapes. IEEE Transaction on Pattern Analysis and Machine Intelligence, 14(2), 239–256.

    Article  Google Scholar 

  • Birk, A., Pathak, K., Vaskevicius, N., Pfingsthorn, M., Poppinga, J., & Schwertfeger, S. (2010). Surface representations for 3D mapping: a case for a paradigm shift. KI—German Journal on Artificial Intelligence.

  • Buelow, H., Birk, A., & Unnithan, V. (2009). Online generation of an underwater photo map with improved Fourier Mellin based registration. In International OCEANS Conference. New York: IEEE Press.

    Google Scholar 

  • Buelow, H., Pfingsthorn, M., & Birk, A. (2010). Using robust spectral registration for scan matching of sonar range data. In 7th symposium on intelligent autonomous vehicles (IAV), IFAC.

    Google Scholar 

  • Castellani, U., Fusiello, A., Murino, V., Papaleo, L., Puppo, E., Repetto, S., & Pittore, M. (2004). Efficient on-line mosaicing from 3D acoustical images. In OCEANS ’04 (Vol. 2, pp. 670–677).

    Google Scholar 

  • Castellani, U., Fusiello, A., & Murino, V. (2002). Registration of multiple acoustic range views for underwater scene reconstruction. Computer Vision and Image Understanding, 87, 78–89.

    Article  Google Scholar 

  • Chen, Q., Defrise, M., & Deconinck, F. (1994). Symmetric phase-only matched filtering of Fourier-Mellin transforms for image registration and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16, 1156–1168.

    Article  Google Scholar 

  • Clark, D., Bell, J., de Saint-Pern, Y., & Petillot, Y. (2005). PHD filter multi-target tracking in 3D sonar. In Oceans 2005 (pp. 265–270).

    Google Scholar 

  • Davison, J., & Kita, N. (2001) 3D simultaneous localisation and map-building using active vision for a robot moving on undulating terrain. In IEEE conference on computer vision and pattern recognition, Hawaii, Dec 8–14, 2001.

  • Driscoll, J., & Healy, D. (1994). Computing Fourier transforms and convolutions on the 2-sphere. Advances in Applied Mathematics, 15, 202–250.

    Article  MathSciNet  MATH  Google Scholar 

  • Ende, B. A. A. (2001). 3D mapping of underwater caves. IEEE Computer Graphics and Applications, 21, 14–20.

    Article  Google Scholar 

  • Fairfield, N., Kantor, G. A., & Wettergreen, D. (2007). Real-time SLAM with octree evidence grids for exploration in underwater tunnels. Journal of Field Robotics, 24(1–2), 3–21.

    Article  Google Scholar 

  • Hähnel, D., Burgard, W., & Thrun, S. (2003). Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems, 44(1), 15–27.

    Article  Google Scholar 

  • Hernandez, E., Ridao, P., Ribas, D., & Mallios, A. (2009). Probabilistic sonar scan matching for an AUV. In Intelligent robots and systems, 2009. IROS 2009. IEEE/RSJ international conference on (pp. 255–260).

    Chapter  Google Scholar 

  • Horn, B. (1984). Extended Gaussian images. Proceedings of the IEEE, 1671–1686.

  • Horner, J. L., & Gianino, P. D. (1984). Phase-only matched filtering. Applied Optics, 23, 812–816.

    Article  Google Scholar 

  • Howard, A., Wolf, D. F., & Sukhatme, G. S. (2004). Towards 3D mapping in large urban environments. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), Sendai, Japan.

    Google Scholar 

  • Keller, Y., Shkolnisky, Y., & Averbuch, A. (2005). Algebraically accurate volume registration using Euler’s theorem and the 3d pseudo-polar fft. In Proc. IEEE conf. comput. vision pattern recognit.

    Google Scholar 

  • Keller, Y., Shkolnisky, Y., & Averbuch, A. (2006). Volume registration using the 3-d pseudopolar Fourier transform. IEEE Transactions on Signal Processing, 54(11), 4323–4331.

    Article  Google Scholar 

  • Kostelec, P., & Rockmore, D. (2003). Ffts on the rotation group. Working Papers Series, Santa Fe Institute.

  • Lee, T. S., Choi, J. S., Lee, J. H., & Lee, B. H. (2009). 3-D terrain covering and map building algorithm for an AUV. In Proceedings of the 2009 IEEE/RSJ international conference on intelligent robots and systems (pp. 4420–4425). St. Louis: IEEE Press.

    Chapter  Google Scholar 

  • Liu, Y., Emery, R., Chakrabarti, D., Burgard, W., & Thrun, S. (2001). Using EM to learn 3D models of indoor environments with mobile robots. In 18th conf. on machine learning, Williams College, 2001.

  • Lorenson, A., & Kraus, D. (2009). 3D-Sonar image formation and shape recognition techniques. In OCEANS 2009 (pp. 1–6).

    Chapter  Google Scholar 

  • Lucchese, L., Doretto, G., & Cortelazzo, G. (2002). A frequency domain technique for range data registration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 1468–1484.

    Article  Google Scholar 

  • Madjidi, H., & Nagahdaripour, S. (2003). 3-D photo-mosaicking of benthic environments. In S. Nagahdaripour (Ed.), OCEANS 2003. Proceedings (Vol. 4, pp. 2317–2318).

    Google Scholar 

  • Majumder, S., Rosenblatt, J., Scheding, S., & Durrant-Whyte, H. (2001). Map building and localization for underwater navigation. In Lecture notes in control and information sciences: Vol. 271. Experimental robotics VII (pp. 511–520). Berlin: Springer.

    Chapter  Google Scholar 

  • Makadia, A., & Daniilidis, K. (2003). Direct 3d-rotation estimation from spherical images via a generalized shift theorem. In Computer vision and pattern recognition, Proceedings. IEEE computer society conference on (Vol. 2, pp. 217–224).

    Google Scholar 

  • Makadia, A., Patterson, A., & Daniilidis, K. (2006). Fully automatic registration of 3D point clouds. In IEEE comp. soc. conf. on computer vision and pattern recognition (CVPR) (Vol. 1, pp. 1297–1304).

    Google Scholar 

  • Makadia, A., Sorgi, L., & Daniilidis, K. (2004). Rotation estimation from spherical images. In Proc. int. conf. on pattern recognition, Cambridge.

    Google Scholar 

  • Mallios, A., Ridao, P., Hernandez, E., Ribas, D., Maurelli, F., & Petillot, Y. (2009). Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar. In OCEANS 2009 (pp. 1–6).

    Chapter  Google Scholar 

  • Murino, V., Fusiello, A., Iuretigh, N., & Puppo, E. (2000). 3D mosaicing for environment reconstruction. In Pattern recognition, 2000. Proceedings. 15th international conference on (Vol. 3, pp. 358–362).

    Chapter  Google Scholar 

  • Murino, V., Palmese, M., & Trucco, A. (2007). Processing tools for acoustic 3D images. In P. Blondel & A. Caiti (Eds.), Springer praxis books: Part II. Buried waste in the seabead—acoustic imaging and bio-toxicity (pp. 39–64). Berlin: Springer.

    Chapter  Google Scholar 

  • Murino, V., & Trucco, A. (2000). Three-dimensional image generation and processing in underwater acoustic vision. Proceedings of the IEEE, 88(12), 1903–1948.

    Article  Google Scholar 

  • Negahdaripour, S., & Madjidi, H. (2003). Stereovision imaging on submersible platforms for 3-D mapping of benthic habitats and sea-floor structures. Oceanic Engineering, IEEE Journal of, 28(4), 625–650.

    Article  Google Scholar 

  • Newman, P., & Durrant-Whyte, H. (1998). Using sonar in terrain-aided underwater navigation. In H. Durrant-Whyte (Ed.), Robotics and automation, 1998. Proceedings. 1998 IEEE international conference on (Vol. 1, pp. 440–445).

    Google Scholar 

  • Nicosevici, T., Gracias, N., Negahdaripour, S., & Garcia, R. (2009). Efficient three-dimensional scene modeling and mosaicing. Journal of Field Robotics, 26(10), 759–788.

    Article  Google Scholar 

  • Nuechter, A., Lingemann, K., Hertzberg, J., & Surmann, H. (2006). 6D SLAM—Mapping outdoor environments. In IEEE international workshop on safety, security, and rescue robotics (SSRR). New York: IEEE Press.

    Google Scholar 

  • Oppenheim, A. V., & Schafer, R. W. (1989). Discrete-time signal processing. Prentice Hall signal processing series. Prentice Hall: Englewood Cliffs.

    MATH  Google Scholar 

  • Oskard, D., Hong, T. H., & Shaffer, C. (1990). Real-time algorithms and data structures for underwater mapping. Systems, Man and Cybernetics, IEEE Transactions on, 20(6), 1469–1475.

    Article  Google Scholar 

  • Palmese, M., & Trucco, A. (2008). From 3-D sonar images to augmented reality models for objects buried on the seafloor. Instrumentation and Measurement, IEEE Transactions on, 57(4), 820–828.

    Article  Google Scholar 

  • Pathak, K., Birk, A., Vaskevicius, N., Pfingsthorn, M., Schwertfeger, S., & Poppinga, J. (2010). Online 3D SLAM by registration of large planar surface segments and closed form pose-graph relaxation. Journal of Field Robotics, Special Issue on 3D Mapping, 27(1), 52–84.

    Google Scholar 

  • Pathak, K., Birk, A., Vaskevicius, N., & Poppinga, J. (2010). Fast registration based on noisy planes with unknown correspondences for 3D mapping. IEEE Transactions on Robotics, 26(3), 424–441.

    Article  Google Scholar 

  • Pathak, K., Vaskevicius, N., & Birk, A. (2010). Uncertainty analysis for optimum plane extraction from noisy 3D range-sensor point-clouds. Intelligent Service Robotics, 3, 37–48.

    Article  Google Scholar 

  • Pfingsthorn, M., Schwertfeger, S., Buelow, H., & Birk, A. (2010). Maximum likelihood mapping with spectral image registration. In IEEE international conference on robotics and automation (ICRA). New York: IEEE Press.

    Google Scholar 

  • Pizarro, O., Eustice, R., & Singh, H. (2004). Large area 3D reconstructions from underwater surveys. In OCEANS, MTS/IEEE conference and exhibition, Kobe, Japan (pp. 678–687).

    Google Scholar 

  • Ribas, D., Ridao, P., Domingo Tardos, J., & Neira, J. (2007). Underwater SLAM in a marina environment. In P. Ridao (Ed.), Intelligent robots and systems, 2007. IROS 2007. IEEE/RSJ International Conference on (pp. 1455–1460).

    Chapter  Google Scholar 

  • Ribas, D., Ridao, P., Neira, J., & Tardos, J. D. (2006). SLAM using an imaging sonar for partially structured underwater environments. In P. Ridao (Ed.), Intelligent robots and systems, 2006 IEEE/RSJ international conference on (pp. 5040–5045).

    Chapter  Google Scholar 

  • Roman, C., & Singh, H. (2006). Consistency based error evaluation for deep sea bathymetric mapping with robotic vehicles. In Robotics and automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on (pp. 3568–3574).

    Chapter  Google Scholar 

  • Roman, C., & Singh, H. (2007). A self-consistent bathymetric mapping algorithm. Journal of Field Robotics, 24(1–2), 23–50.

    Article  Google Scholar 

  • Saez, J., Hogue, A., Escolano, F., & Jenkin, M. (2006). Underwater 3D SLAM through entropy minimization. In Robotics and automation, 2006. ICRA 2006. Proceedings 2006 IEEE international conference on (pp. 3562–3567).

    Chapter  Google Scholar 

  • Salvi, J., Petillot, Y., Thomas, S., & Aulinas, J. (2008). Visual SLAM for underwater vehicles using video velocity log and natural landmarks. In OCEANS 2008 (pp. 1–6).

    Chapter  Google Scholar 

  • Santilli, K., Bemis, K., Silver, D., Dastur, J., & Rona, P. (2004). Generating realistic images from hydrothermal plume data. In Visualization, 2004 (pp. 91–98). New York: IEEE.

    Chapter  Google Scholar 

  • Sedlazeck, A., Koeser, K., & Koch, R. (2009). 3D reconstruction based on underwater video from ROV Kiel 6000 considering underwater imaging conditions. In IEEE OCEANS Conference ’09, Bremen, Germany.

    Google Scholar 

  • Surmann, H., Nuechter, A., & Hertzberg, J. (2003). An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Robotics and Autonomous Systems, 45(3–4), 181–198.

    Article  Google Scholar 

  • Thrun, S., Haehnel, D., Ferguson, D., Montemerlo, M., Triebel, R., Burgard, W., Baker, C., Omohundro, Z., Thayer, S., & Whittaker, W. (2003). A system for volumetric robotic mapping of abandoned mines. In Proc. IEEE international conference on robotics and automation (ICRA), Taipei, Taiwan.

    Google Scholar 

  • Vaskevicius, N., Birk, A., Pathak, K., Schwertfeger, S., & Rathnam, R. (2010). Efficient representation in 3D environment modeling for planetary robotic exploration. Advanced Robotics, 24(8–9).

    Google Scholar 

  • Weingarten, J. (2006). Feature-based 3D SLAM. PhD thesis, EPFL, Lausanne, Switzerland.

  • Williams, S., & Mahon, I. (2004). Simultaneous localisation and mapping on the Great Barrier Reef. In I. Mahon (Ed.), Robotics and automation, 2004. Proceedings. ICRA ’04. 2004 IEEE international conference on (Vol. 2, pp. 1771–1776).

    Chapter  Google Scholar 

  • Wulf, O., Brenneke, C., & Wagner, B. (2004). Colored 2D maps for robot navigation with 3D sensor data. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 3, pp. 2991–2996). New York: IEEE Press.

    Google Scholar 

  • Wulf, O., & Wagner, B. (2003). Fast 3D-scanning methods for laser measurement systems. In International conference on control systems and computer science (CSCS14).

  • Yu, S. C., Kim, T. W., Marani, G., & Choi, S. (2007). Real-time 3D sonar image recognition for underwater vehicles. In Underwater technology and workshop on scientific use of submarine cables and related technologies, 2007. Symposium on (pp. 142–146).

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Dept. of Computer Science, Jacobs University Bremen, 28759, Bremen, Germany

    Heiko Bülow & Andreas Birk

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  1. Heiko Bülow
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  2. Andreas Birk
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Correspondence to Andreas Birk.

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Bülow, H., Birk, A. Spectral registration of noisy sonar data for underwater 3D mapping. Auton Robot 30, 307–331 (2011). https://doi.org/10.1007/s10514-011-9221-8

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