P. Dinnissen, S. N. Givigi, and H. M. Schwartz, Map merging of Multi-Robot SLAM using Reinforcement Learning, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012.
DOI : 10.1109/ICSMC.2012.6377676

A. Nüchter, K. Lingemann, J. Hertzberg, and H. Surmann, 6D SLAM???3D mapping outdoor environments, Journal of Field Robotics, vol.23, issue.8-9, 2007.
DOI : 10.1109/TRO.2004.829506

S. Kohlbrecher, O. V. Stryk, T. U. Darmstadt, J. Meyer, and U. Klingauf, A flexible and scalable SLAM system with full 3D motion estimation, 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, 2011.
DOI : 10.1109/SSRR.2011.6106777
URL : http://www.sim.informatik.tu-darmstadt.de/publ/download/2011_SSRR_KohlbrecherMeyerStrykKlingauf_Flexible_SLAM_System.pdf

F. Pomerleau, F. Colas, R. Siegwart, and S. Magnenat, Comparing ICP variants on real-world data sets -open-source library and experimental protocol, Auton. Robots, 2013.
DOI : 10.1007/s10514-013-9327-2
URL : https://hal.archives-ouvertes.fr/hal-01143458

R. Zlot and M. Bosse, Efficient Large-Scale 3D Mobile Mapping and Surface Reconstruction of an Underground Mine, FSR, ser. Springer Tracts in Advanced Robotics
DOI : 10.1007/978-3-642-40686-7_32

M. Bosse, R. Zlot, and P. Flick, Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping, IEEE Transactions on Robotics, vol.28, issue.5, 2012.
DOI : 10.1109/TRO.2012.2200990

M. Bosse and R. Zlot, Continuous 3D scan-matching with a spinning 2D laser, 2009 IEEE International Conference on Robotics and Automation, 2009.
DOI : 10.1109/ROBOT.2009.5152851

J. Zhang and S. Singh, Loam: Lidar odometry and mapping in realtime, Robotics: Science and Systems, 2014.
DOI : 10.15607/rss.2014.x.007
URL : http://roboticsproceedings.org/rss10/p07.pdf

M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit, Fastslam 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges, Proc. of the Int. Conf. on Artificial Intelligence (IJCAI, 2003.

J. Jessup, S. N. Givigi, and A. Beaulieu, Merging of octree based 3D occupancy grid maps, 2014 IEEE International Systems Conference Proceedings, 2014.
DOI : 10.1109/SysCon.2014.6819283

P. Payeur, P. Hebert, D. Laurendeau, and C. M. Gosselin, Probabilistic octree modeling of a 3D dynamic environment, Proceedings of International Conference on Robotics and Automation, 1997.
DOI : 10.1109/ROBOT.1997.614315

J. Fournier, B. Ricard, and D. Laurendeau, Mapping and Exploration of Complex Environments Using Persistent 3D Model, Fourth Canadian Conference on Computer and Robot Vision (CRV '07), 2007.
DOI : 10.1109/CRV.2007.45

K. Pathak, A. Birk, S. Schwertfeger, and J. Poppinga, 3D forward sensor modeling and application to occupancy grid based sensor fusion, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007.
DOI : 10.1109/IROS.2007.4399406
URL : http://robotics.jacobs-university.de/publications/IROS07_3D-RangeSensorFusion.pdf

N. Fairfield, G. Kantor, and D. Wettergreen, Real-Time SLAM with Octree Evidence Grids for Exploration in Underwater Tunnels, Journal of Field Robotics, vol.12, issue.1-2, 2007.
DOI : 10.1002/rob.20165

A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, OctoMap: an efficient probabilistic 3D mapping framework based on octrees, Autonomous Robots, vol.11, issue.3, 2013.
DOI : 10.15607/RSS.2007.III.017
URL : http://www.informatik.uni-freiburg.de/~stachnis/pdf/hornung13auro.pdf

A. Hornung, K. M. Wurm, and M. Bennewitz, Humanoid robot localization in complex indoor environments, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010.
DOI : 10.1109/IROS.2010.5649751
URL : http://ais.informatik.uni-freiburg.de/publications/papers/hornung10iros.pdf

J. Jessup, S. N. Givigi, A. E. Beaulieu18-]-n, H. L. Ozkucur, and . Akin, Robust and efficient multirobot 3-d mapping merging with octree-based occupancy grids Supervised feature type selection for topological mapping in indoor environments, 21st Signal Processing and Communications Applications Conference, pp.1-4, 2013.

J. Zhang and S. Singh, Visual-lidar odometry and mapping: low-drift, robust, and fast, 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015.
DOI : 10.1109/ICRA.2015.7139486
URL : http://www.frc.ri.cmu.edu/%7Ejizhang03/Publications/ICRA_2015.pdf

R. I. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision, 2004.
DOI : 10.1017/CBO9780511811685

R. Rusu and S. Cousins, 3D is here: Point Cloud Library (PCL), 2011 IEEE International Conference on Robotics and Automation, 2011.
DOI : 10.1109/ICRA.2011.5980567
URL : http://www.pointclouds.org/assets/pdf/pcl_icra2011.pdf

S. Rusinkiewicz and M. Levoy, Efficient variants of the ICP algorithm, Proceedings Third International Conference on 3-D Digital Imaging and Modeling, 2001.
DOI : 10.1109/IM.2001.924423

P. J. Besl and N. D. Mckay, A method for registration of 3-D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.14, issue.2, 1992.
DOI : 10.1109/34.121791