22 lines
2.5 KiB
BibTeX
22 lines
2.5 KiB
BibTeX
@article{performance ,
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language = {English},
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copyright = {Copyright 2015, The Institution of Engineering and Technology},
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title = {Performance Analysis of the Microsoft Kinect Sensor for 2D Simultaneous Localization and Mapping (SLAM) Techniques},
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journal = {Sensors},
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journal = {Sensors (Switzerland)},
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author = {Kamarudin, K. and Mamduh, S.M. and Shakaff, A.Y.M. and Zakaria, A.},
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volume = { 14},
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number = { 12},
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year = {2014/12/},
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pages = {23365 - 87},
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issn = {1424-8220},
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address = {Switzerland},
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abstract = {This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect's depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks.},
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keywords = {control engineering computing;Linux;mobile robots;optical scanners;real-time systems;SLAM (robots);virtual machines;},
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note = {performance analysis;Microsoft kinect sensor;2D simultaneous localization and mapping technique;SLAM technique;laser scanner-based simultaneous localization and mapping technique;Gmapping;hector SLAM;laser sensor;system integration approach;Linux virtual machine;open source SLAM algorithm;office corridor;real time SLAM operation;system implementation;Windows-based SLAM algorithm;robot operating system;ROS;laser scanner-based parameter;map accuracy;Kinect depth sensor;2D SLAM task;},
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URL = {http://dx.doi.org/10.3390/s141223365},
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}
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