Extracting Landmarks with 3D Information for Simultaneous Localization and mapping(SLAM) using Kinect

Zahan, Gazi Md. Hasnat; Zunaid, A.F.M.

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dc.contributor.author	Zahan, Gazi Md. Hasnat
dc.contributor.author	Zunaid, A.F.M.
dc.date.accessioned	2021-09-10T09:46:46Z
dc.date.available	2021-09-10T09:46:46Z
dc.date.issued	2013-11-15
dc.identifier.citation	R. C. Smith and P. Cheeseman. On the representation and estimation of Spatial uncertainty. Int. J. of Robotics Research, 1986. H. Durrant-Whyte, Tim Bailey. Simultaneous Localisation and Mapping(SLAM):Part I The Essential Algorithms. S. B. Willimas, G. Dissanauake, H. Durrant-Whyte. An E cient Approach to the Simultaneous Localisation and Mapping Problem. Australia, 2006. J.A. Castellanos, J.M.M. Montiel, J. Neira, and J.D. Tar-dos. Sensor in?uence in the performance of simultaneous mo-bile robot localization and map building. In P. Corke and J. Trevelyan, editors, Experimental Robotics IV, pages 287-296. Springer-Verlag, 2000. M.W.M.G. Dissanayake, P. Newman, H.F. Durrant-Whyte, S. Clark, and M. Csorba. An experimental and theoreticalinvestigation into simultaneous localisation and map building.Experimental Robotics IV, pages 265-274, 2000. H.J.S. Feder, J.J. Leonard, and C.M. Smith. Adaptive mo-bile robot navigation and mapping. International Journal of Robotics Research, Special Issue on Field and Service Robotics, 18(7):650-668, 1999 J.J. Leonard and H.J.S. Feder. A computationally e?cient method for large-scale concurrent mapping and localization. In Proc. Ninth International Symposium on Robotics Research, pages 169-176. International Foundation of Robotics Research, 1999 35 Chapter 8. Reference 36 P. Newman. On The Structure and Solution of the Simultaneous Localisation and Map Building Problem. PhD thesis, University of Sydney, Australian Centre for Field Robotics, 1999. S. Thrun, D. Fox, and W. Burgard. A probabilistic approach to concurrent mapping and localization for mobile robots. Machine Learning and Autonomous Robots (joint issue), 1998. S.B. Williams, G. Dissanayake, and H.F. Durrant-Whyte. To-wards terrain-aided navigation for underwater robotics. Ad-vanced Robotics, 15(5):533-550, 2001. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit. FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges. G. Dissanayake, S. Huang, Z. Wang, R. Ranashinghe. A Review of Recent Developments in Simultaneous Localization and Mapping. Sri Lanka,2011. M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit. FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem. G. Hu, S. HuANG, l. Zhao, A. Alempijevic, G. Dissanayake. A Robust RGB-D SLAM Algorithm. Int. Con. On Intelligent Robotics and Systems, 2012. A. T. Norton, A. R. McCook. Implementation of Simultaneous Localisation and mapping algorithm in an Autonomous Robot.2012 J. E. Guivant and E. M. Nebot, "Optimization of the simultaneous localization and map building (SLAM) algorithm for real time implemen-tation," IEEE Transactions on Robotics and Automation, 17(3):242-257, 2001.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/956
dc.description	Supervised by Md. Kamrul Hasan Ph.D Thesis Supervisor, Assistant Professor, Department of Computer Science and Engineering, Islamic University of Technology. Mr. Hasan Mahmud Thesis Co-Supervisor, Assistant Professor, Department of Computer Science and Engineering, Islamic University of Technology.	en_US
dc.description.abstract	SLAM is one of the most widely researched sub elds of robotics. An intuitive understanding of the SLAM process can be conveyed though a hypothetical ex- ample. Consider a simple mobile robot: a set of wheels connected to a motor and a camera, complete with actuators-physical devices for controlling the speed and direction of the unit. Now imagine the robot being used remotely by an operator to map inaccessible places. The actuators allow the robot to move around, and the camera provides enough visual information for the operator to understand where surrounding objects are and how the robot is oriented in reference to them. What the human operator is doing is an example of SLAM (Simultaneous Local- ization and Mapping). Determining the location of objects in the environment is an instance of mapping, and establishing the robot position with respect to these objects is an example of localization. The SLAM sub eld of robotics attempts to provide a way for robots to do SLAM autonomously. A solution to the SLAM problem would allow a robot to make maps without any human assistance What so ever. In this paper we proposed and implement a new technique to increase the e ciency of SLAM using Kinect sensor.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Technical and Vocational Education (TVE), Islamic University of Technology (IUT)	en_US
dc.title	Extracting Landmarks with 3D Information for Simultaneous Localization and mapping(SLAM) using Kinect	en_US
dc.type	Thesis	en_US