@inproceedings{scholars10989,
            year = {2019},
       publisher = {IOP Publishing Ltd},
         journal = {IOP Conference Series: Materials Science and Engineering},
             doi = {10.1088/1757-899X/705/1/012037},
          number = {1},
            note = {cited By 19; Conference of 5th International Conference on Man Machine Systems, ICoMMS 2019 ; Conference Date: 26 August 2019 Through 27 August 2019; Conference Code:156622},
          volume = {705},
           title = {Analysis of Mobile Robot Indoor Mapping using GMapping Based SLAM with Different Parameter},
          author = {Norzam, W. A. S. and Hawari, H. F. and Kamarudin, K.},
            issn = {17578981},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078316978&doi=10.1088\%2f1757-899X\%2f705\%2f1\%2f012037&partnerID=40&md5=6af337c217db46829126b8306bf09fb1},
        keywords = {Indoor positioning systems; Interactive computer systems; Machinery; Man machine systems; Mapping; Mobile robots; Monte Carlo methods; Particle size analysis; Range finders, Laser range finders; Laser sensor; Mapping accuracy; Mapping method; Particle filter; Robot location; Simultaneous localization and mapping; Wireless communications, SLAM robotics},
        abstract = {Mapping is one of the elemental application of the mobile robot. The map is created using the mobile robot by employing sensors such as camera, sonar and laser sensor. One of the most popular mapping methods is the Simultaneous Localization and Mapping (SLAM). SLAM allows the map to be created while localizing the robot location in the map at the same time. GMapping is one of the widely used algorithms in SLAM which will be used in this project. The mobile robot is equipped with a Hokuyo Laser Range Finder sensor and netbook. The router is used for wireless communication between the mobile robot and the user. The GMapping is done in two different locations of different lab size and amount of features in the area. Three trial is conducted to investigate the effects of different parameters such as robot speed, mapping delay and particle filter on the mapping quality. The results show a significant difference in terms of mapping accuracy and the time taken to complete the process as the parameter changed from the three trial. As a result, the parameter used in the second trial, robot speed 0.1333m/s, mapping delay 1s and particle filter 30 is considered as the best based on the time taken and the map accuracy. {\^A}{\copyright} Published under licence by IOP Publishing Ltd.}
}