EVALUATION OF SCENE PARAMETERS FOR OPTIMUM PERFORMANCE OF LOCALIZATION USING KINECT
DOI:
https://doi.org/10.11113/jt.v76.5862Keywords:
Kinect sensor, MATLAB, high speed vision system, point matching featuresAbstract
The needs of having locations identification are so important in this technological era. Locations identification has been implemented in so many areas nowadays. The challenge in this system is to identify location in real-time. Here, real-time refers to the process of capturing the location and recognize it at the same time. This research proposes a location identification system using Kinect, which is a high speed vision sensor that has been used for Xbox 360 video games. This research is to evaluate the optimum image detection distance, image size and processing time in identifying the locations that have been trained. The algorithm uses the Point Matching Features from the MATLAB software. From here, features are generated from both real time and training images being compared and the matching points between them will be used to identify the locations.
References
Tamura, Y., Takabatake, Y., Kashima, N. and Umetani, T. 2012. Localization System using Microsoft Kinect for Indoor Structures. The Japan Society of Plasma Science and Nuclear Fusion Research, Konan University, Japan, 7: 2406036-1- 2406036-4. DOI: 10.1585/pfr.7.2406036.
Yii, W., Damayanthi, N., Drummond, T. and Li, W. H. 2014. Visual Localization of a Robot with an external RGBD Sensors. Monash University, Australia retrieved October 12, 2014 from https://ssl.linklings.net/conferences/acra/program/attendee_program_acra2011/includes/files/pap157.pdf.
Correa, D. S. O., Sciotti, D. F., Prado, M. G., Sales, D. O., Wolf, D. F. and Osorio, F. S. 2012. Mobile Robots Navigation in Indoor Environments Using Kinect Sensor. Second Brazilian Conference on Critical Embedded Systems. 36-41. DOI 10.1109/CBSEC.2012.18.
Rivera, L. A., DeSouza, G. N. and Franklin L. D. R. 2013. Control of a Wheelchair using an Adaptive Kmeans Clustering. IEEE Symposium on Computational Intelligence in Rehabilitation and Assistive Technologies (CIRAT), University of Missouri, Columbia. 24-31.
Keat, H. W. and Ming, L. S. 2012. An Investigation of the Use of Kinect Sensor for Indoor Navigation. IEEE Region 10 Conference (TENCON 2012), Monash University Sunway campus, Malaysia. 1-5. DOI 10.1109/TENCON. 2012. 6412246.
Khoshelham, K. 2011. Accuracy Analysis of Kinect Depth Data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Calgary 2011 Workshop), University of Twente, Canada. 133-138.
Tolgyessy, M. and Hubinsky, P. 2014. The Kinect Sensor in Robotics Education. Slovak University of Technology in Bratislava, Slovakia retrieved October, 12, 2014 from http://www.innoc.at/fileadmin/user_upload/_temp_/RiE/Proceedings/69.pdf.
Ajay, A. and Venkataraman, D. 2013. A Survey on Sensing Methods and Features Extraction Algorithms for SLAM Problem. International Journal of Computer Science, Engineering and Applications (IJCSEA). 3(1): 59-63. DOI: 10.5121/ijcsea.2013.3105.
Ilias, B., Abdul Shukor, S. A., Yaacob, S., Adom, A. H. and Mohd Razali, M. H. 2014. A Nurse Following Robot with High Speed Kinect Sensor. ARPN Journal of Engineering and Applied Sciences. 9(12): 2454-2459. ISSN 1819-6608.
Emter, T. and Stein, A. 2012. Simultaneous Localization and Mapping with the Kinect Sensor. Proceedings of ROBOTIK, 7th German Conference on Robotics. 1-6.
Zhang, Z. 2012. Microsoft Kinect Sensor and Its Effect. Multimedia. IEEE. 19(2): 4-10. DOI:10.1109/MMUL.2012.24.
Pradalier, C., and Sekhavat, S. 2002. Current Matching, Localization and Map Building using Invariant Features. Proceedings of the 2002 IEEE/RSJ. Intl. Conference on Intelligent Robots and Systems, Switzerland. 514-520.
Andreopoulos, A. and Tsotsos, J. K. 2013. 50 Years of Object Recognition Directions Forward. Computer Vision and Image Understanding. 117(8): 827-891. DOI: 10.1016/j.cviu.2013.04.005.
Hernandez-Lopez, J. J., Quintanilla-Olvera, A. L., Lopez-Ramırez, J. L., Rangel-Butanda, F. J., Ibarra-Manzano, M. A and Almanza-Ojeda, D. L. 2012. Detecting Objects Using Color and Depth Segmentation with Kinect Sensor. The 2012 Iberoamerican Conference on Electronics Engineering and Computer Science. 196-204. DOI:10.1016/j.protcy.2012.03.021.
Biswas, J. and Veloso, M. 2012. Depth Camera Based Indoor Mobile Robot Localization and Navigation. Proceedings of IEEE International Conference on Robotics and Automation. 1697-1702.
Cunha, J., Pedrosa, E., Cruz, C., Neves, A. J. R. and Lau, N. 2011. Using a Depth Camera for Indoor Localization and Navigation. Retrieved October, 12, 2014 from http://mobilerobotics.cs.washington.edu/rgbd-workshop-2011/camera_ready/cunha-rgbd11-localization.pdf.
Hadjakos, A. and Darmstadt, T. 2012. Pianist Motion Capture with the Kinect Depth Data. Proceedings of the 9th Sound and Music Computing Conference, Copenhagen, Denmark. 303-310.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
