Abstract
This paper presents a technique for improved mapping of complex underwater environments. Autonomous underwater vehicles (AUVs) are becoming valuable tools for inspection of underwater infrastructure, and can create 3D maps of their environment using high-frequency profiling sonar. However, the quality of these maps is limited by the drift in the vehicle’s navigation system.We have developed a technique for simultaneous localization and mapping (SLAM) by aligning point clouds gathered over a short time scale using the iterative closest point (ICP) algorithm. To improve alignment, we have developed a system for smoothing these “submaps” and removing outliers. We integrate the constraints from submap alignment into a 6-DOF pose graph, which is optimized to estimate the full vehicle trajectory over the duration of the inspection task. We present real-world results using the Bluefin Hovering AUV, as well as analysis of a synthetic data set.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alexa, M., Behr, J., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.T.: Computing and rendering point set surfaces. IEEE Transactions on Visualization and Computer Graphics 9(1), 3–15 (2003)
Barkby, S., Williams, S., Pizarro, O., Jakuba, M.: An efficient approach to bathymetric SLAM. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems, IROS (2009)
Beall, C., Dellaert, F., Mahon, I., Williams, S.: Bundle adjustment in large-scale 3D reconstructions based on underwater robotic surveys. In: Proc. of the IEEE/MTS OCEANS Conf. and Exhibition (2011)
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Machine Intell. 14(2), 239–256 (1996)
Borrmann, D., Elseberg, J., Lingemann, K., Nuchter, A., Hertzberg, J.: Globally consistent 3D mapping with scan matching. J. of Robotics and Autonomous Systems 56(2), 130–142 (2008)
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: SIGGRAPH, pp. 303–312 (1996)
Dellaert, F., Kaess, M.: Square Root SAM: Simultaneous localization and mapping via square root information smoothing. Intl. J. of Robotics Research 25(12), 1181–1203 (2006)
Englot, B., Hover, F.: Sampling-based coverage path planning for inspection of complex structures. In: Proc. of Intl. Conf. on Automated Planning and Scheduling (2012)
Eustice, R., Singh, H., Leonard, J., Walter, M., Ballard, R.: Visually navigating the RMS Titanic with SLAM information filters. In: Robotics: Science and Systems, RSS (2005)
Fairfield, N., Kantor, A.G., Wettergreen, D.: Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J. of Field Robotics (2007)
Folkesson, J., Leonard, J.: Autonomy through SLAM for an underwater robot. In: Proc. of the Intl. Symp. of Robotics Research, ISRR (2009)
Hover, F., Eustice, R., Kim, A., Englot, B., Johannsson, H., Kaess, M., Leonard, J.: Advanced perception, navigation and planning for autonomous in-water ship hull inspection. Intl. J. of Robotics Research 31(12), 1445–1464 (2012)
Kaess, M., Ranganathan, A., Dellaert, F.: iSAM: Incremental smoothing and mapping. IEEE Trans. Robotics 24(6), 1365–1378 (2008)
Kunz, C., Singh, H.: Map building fusing acoustic and visual information using autonomous underwater vehicles. J. of Field Robotics 30(5), 763–783 (2013)
Nüchter, A., Hertzberg, J.: Towards semantic maps for mobile robots. J. of Robotics and Autonomous Systems 56(11), 915–926 (2008), doi: http://dx.doi.org/10.1016/j.robot.2008.08.001
Ribas, D., Ridao, P., Tardós, J., Neira, J.: Underwater SLAM in man-made structured environments. Journal of Field Robotics 25(11-12), 898–921 (2008)
Roman, C., Singh, H.: Improved vehicle based multibeam bathymetry using sub-maps and SLAM. In: IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), pp. 3662–3669 (2005)
Roman, C., Singh, H.: A self-consistent bathymetric mapping algorithm. J. of Field Robotics 24(1), 23–50 (2007)
Rusu, R., Cousins, S.: 3D is here: Point Cloud Library (PCL). In: IEEE Intl. Conf. on Robotics and Automation (ICRA), Shanghai, China (2011)
Rusu, R.B., Marton, Z.C., Blodow, N., Dolha, M., Beetz, M.: Towards 3D point cloud based object maps for household environments. J. of Robotics and Autonomous Systems 56(11), 927–941 (2008)
Thrun, S., Liu, Y., Koller, D., Ng, A., Ghahramani, Z., Durrant-Whyte, H.: Simultaneous localization and mapping with sparse extended information filters. Intl. J. of Robotics Research 23(7) (2004)
Walter, M., Hover, F., Leonard, J.: SLAM for ship hull inspection using exactly sparse extended information filters. In: IEEE Intl. Conf. on Robotics and Automation (ICRA), pp. 1463–1470 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this chapter
Cite this chapter
VanMiddlesworth, M., Kaess, M., Hover, F., Leonard, J.J. (2015). Mapping 3D Underwater Environments with Smoothed Submaps. In: Mejias, L., Corke, P., Roberts, J. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-319-07488-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-07488-7_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07487-0
Online ISBN: 978-3-319-07488-7
eBook Packages: EngineeringEngineering (R0)