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A Dense Subframe-based SLAM Framework with Side-scan Sonar (2312.13802v2)

Published 21 Dec 2023 in cs.RO

Abstract: Side-scan sonar (SSS) is a lightweight acoustic sensor that is commonly deployed on autonomous underwater vehicles (AUVs) to provide high-resolution seafloor images. However, leveraging side-scan images for simultaneous localization and mapping (SLAM) presents a notable challenge, primarily due to the difficulty of establishing sufficient amount of accurate correspondences between these images. To address this, we introduce a novel subframe-based dense SLAM framework utilizing side-scan sonar data, enabling effective dense matching in overlapping regions of paired side-scan images. With each image being evenly divided into subframes, we propose a robust estimation pipeline to estimate the relative pose between each paired subframes, by using a good inlier set identified from dense correspondences. These relative poses are then integrated as edge constraints in a factor graph to optimize the AUV pose trajectory. The proposed framework is evaluated on three real datasets collected by a Hugin AUV. Among one of them includes manually-annotated keypoint correspondences as ground truth and is used for evaluation of pose trajectory. We also present a feasible way of evaluating mapping quality against multi-beam echosounder (MBES) data without the influence of pose. Experimental results demonstrate that our approach effectively mitigates drift from the dead-reckoning (DR) system and enables quasi-dense bathymetry reconstruction. An open-source implementation of this work is available.

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References (49)
  1. S. Thrun, “Probabilistic Robotics,” Communications of the ACM, vol. 45, no. 3, pp. 52–57, 2002.
  2. A. Palomer, P. Ridao, and D. Ribas, “Multibeam 3d underwater slam with probabilistic registration,” Sensors, vol. 16, no. 4, p. 560, 2016.
  3. I. Torroba, N. Bore, and J. Folkesson, “Towards autonomous industrial-scale bathymetric surveying,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 6377–6382.
  4. E. Westman, A. Hinduja, and M. Kaess, “Feature-based slam for imaging sonar with under-constrained landmarks,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 3629–3636.
  5. J. Li, M. Kaess, R. M. Eustice, and M. Johnson-Roberson, “Pose-graph slam using forward-looking sonar,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2330–2337, 2018.
  6. E. Westman and M. Kaess, “Degeneracy-aware imaging sonar simultaneous localization and mapping,” IEEE Journal of Oceanic Engineering, vol. 45, no. 4, pp. 1280–1294, 2019.
  7. C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman, “Patchmatch: A randomized correspondence algorithm for structural image editing,” ACM Trans. Graph., vol. 28, no. 3, p. 24, 2009.
  8. J. Zhang, Y. Xie, L. Ling, and J. Folkesson, “A Fully-automatic Side-scan Sonar Simultaneous Localization and Mapping Framework,” IET Radar, Sonar & Navigation, pp. 1–10, 2023.
  9. M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.
  10. J. Aulinas, X. Lladó, J. Salvi, and Y. R. Petillot, “Feature based slam using side-scan salient objects,” in OCEANS 2010 MTS/IEEE SEATTLE.   IEEE, 2010, pp. 1–8.
  11. I. T. Ruiz, Y. Petillot, and D. M. Lane, “Improved auv navigation using side-scan sonar,” in Oceans 2003. Celebrating the Past… Teaming Toward the Future (IEEE Cat. No. 03CH37492), vol. 3.   IEEE, 2003, pp. 1261–1268.
  12. I. T. Ruiz, S. De Raucourt, Y. Petillot, and D. M. Lane, “Concurrent mapping and localization using sidescan sonar,” IEEE Journal of Oceanic Engineering, vol. 29, no. 2, pp. 442–456, 2004.
  13. S. Reed, I. T. Ruiz, C. Capus, and Y. Petillot, “The fusion of large scale classified side-scan sonar image mosaics,” IEEE transactions on image processing, vol. 15, no. 7, pp. 2049–2060, 2006.
  14. M. F. Fallon, M. Kaess, H. Johannsson, and J. J. Leonard, “Efficient auv navigation fusing acoustic ranging and side-scan sonar,” in 2011 IEEE International Conference on Robotics and Automation.   IEEE, 2011, pp. 2398–2405.
  15. S. J. Julier and J. K. Uhlmann, “A counter example to the theory of simultaneous localization and map building,” in Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164), vol. 4.   IEEE, 2001, pp. 4238–4243.
  16. L. Bernicola, D. Gueriot, and J.-M. Le Caillec, “A hybrid registration approach combining slam and elastic matching for automatic side-scan sonar mosaic,” in 2014 Oceans-St. John’s.   IEEE, 2014, pp. 1–5.
  17. M. Kaess, A. Ranganathan, and F. Dellaert, “isam: Incremental smoothing and mapping,” IEEE Transactions on Robotics, vol. 24, no. 6, pp. 1365–1378, 2008.
  18. M. Issartel, D. Guériot, N. Aouf, and J.-M. Le Caillec, “Robust slam for side scan sonar image mosaicking,” in OCEANS 2017-Anchorage.   IEEE, 2017, pp. 1–10.
  19. J. Aulinas, X. Lladó, J. Salvi, and Y. R. Petillot, “Selective submap joining for underwater large scale 6-dof slam,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2010, pp. 2552–2557.
  20. J. Neira and J. D. Tardós, “Data association in stochastic mapping using the joint compatibility test,” IEEE Transactions on robotics and automation, vol. 17, no. 6, pp. 890–897, 2001.
  21. K. Siantidis, “Side scan sonar based onboard slam system for autonomous underwater vehicles,” in 2016 IEEE/OES Autonomous Underwater Vehicles (AUV).   IEEE, 2016, pp. 195–200.
  22. M. F. Fallon, G. Papadopoulos, J. J. Leonard, and N. M. Patrikalakis, “Cooperative auv navigation using a single maneuvering surface craft,” The International Journal of Robotics Research, vol. 29, no. 12, pp. 1461–1474, 2010.
  23. P. Vandrish, A. Vardy, D. Walker, and O. Dobre, “Side-scan sonar image registration for auv navigation,” in 2011 IEEE Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies.   IEEE, 2011, pp. 1–7.
  24. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
  25. P. King, B. Anstey, and A. Vardy, “Comparison of feature detection techniques for auv navigation along a trained route,” in 2013 OCEANS-San Diego.   IEEE, 2013, pp. 1–8.
  26. P. King, A. Vardy, P. Vandrish, and B. Anstey, “Real-time side scan image generation and registration framework for auv route following,” in 2012 IEEE/OES Autonomous Underwater Vehicles (AUV).   IEEE, 2012, pp. 1–6.
  27. C. M. MacKenzie, M. L. Seto, and Y. Pan, “Extracting seafloor elevations from side-scan sonar imagery for slam data association,” in 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE).   IEEE, 2015, pp. 332–336.
  28. J. Petrich, M. F. Brown, J. L. Pentzer, and J. P. Sustersic, “Side scan sonar based self-localization for small autonomous underwater vehicles,” Ocean Engineering, vol. 161, pp. 221–226, 2018.
  29. P. J. Besl and N. D. McKay, “Method for registration of 3-d shapes,” in Sensor fusion IV: control paradigms and data structures, vol. 1611.   Spie, 1992, pp. 586–606.
  30. T. Brox and J. Malik, “Large displacement optical flow: descriptor matching in variational motion estimation,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, no. 3, pp. 500–513, 2010.
  31. P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, “Deepflow: Large displacement optical flow with deep matching,” in Proceedings of the IEEE international conference on computer vision, 2013, pp. 1385–1392.
  32. J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, “Epicflow: Edge-preserving interpolation of correspondences for optical flow,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 1164–1172.
  33. K. He and J. Sun, “Computing nearest-neighbor fields via propagation-assisted kd-trees,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition.   IEEE, 2012, pp. 111–118.
  34. S. Korman and S. Avidan, “Coherency sensitive hashing,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 6, pp. 1099–1112, 2015.
  35. C. Bailer, B. Taetz, and D. Stricker, “Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 4015–4023.
  36. Y. Hu, R. Song, and Y. Li, “Efficient coarse-to-fine patchmatch for large displacement optical flow,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5704–5712.
  37. W. Xu, L. Li, Y. Xie, J. Zhang, and J. Folkesson, “Evaluation of a canonical image representation for sidescan sonar,” in 2023 OCEANS-Limerick.   IEEE, 2023.
  38. E. Coiras, Y. Petillot, and D. M. Lane, “Multiresolution 3-d reconstruction from side-scan sonar images,” IEEE Transactions on Image Processing, vol. 16, no. 2, pp. 382–390, 2007.
  39. A. Burguera and G. Oliver, “Intensity correction of side-scan sonar images,” in Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).   IEEE, 2014, pp. 1–4.
  40. M. D. Aykin and S. Negahdaripour, “Forward-look 2-d sonar image formation and 3-d reconstruction,” in 2013 OCEANS-San Diego.   IEEE, 2013, pp. 1–10.
  41. J. H. Friedman, J. L. Bentley, and R. A. Finkel, “An algorithm for finding best matches in logarithmic expected time,” ACM Transactions on Mathematical Software (TOMS), vol. 3, no. 3, pp. 209–226, 1977.
  42. M. Kaess, H. Johannsson, R. Roberts, V. Ila, J. J. Leonard, and F. Dellaert, “isam2: Incremental smoothing and mapping using the bayes tree,” The International Journal of Robotics Research, vol. 31, no. 2, pp. 216–235, 2012.
  43. J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in 2012 IEEE/RSJ international conference on intelligent robots and systems.   IEEE, 2012, pp. 573–580.
  44. N. Bore and J. Folkesson, “Neural shape-from-shading for survey-scale self-consistent bathymetry from sidescan,” IEEE Journal of Oceanic Engineering, 2022.
  45. Y. Xie, N. Bore, and J. Folkesson, “Neural network normal estimation and bathymetry reconstruction from sidescan sonar,” IEEE Journal of Oceanic Engineering, vol. 48, no. 1, pp. 218–232, 2022.
  46. ——, “Bathymetric reconstruction from sidescan sonar with deep neural networks,” IEEE Journal of Oceanic Engineering, vol. 48, no. 2, pp. 372–383, 2023.
  47. B. Jalving, K. Gade, O. K. Hagen, and K. Vestgard, “A toolbox of aiding techniques for the hugin auv integrated inertial navigation system,” in Oceans 2003. Celebrating the Past… Teaming Toward the Future (IEEE Cat. No. 03CH37492), vol. 2.   IEEE, 2003, pp. 1146–1153.
  48. K. Gade, “Navlab: overview and user guide november 2003,” 2003.
  49. D. Sun, S. Roth, and M. J. Black, “A quantitative analysis of current practices in optical flow estimation and the principles behind them,” International Journal of Computer Vision, vol. 106, pp. 115–137, 2014.
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