Local Generating Map System Using Rviz ROS and Kinect Camera for Rescue Robot Application

Authors

  • Syahri Muharom Institut Teknologi Adhi Tama Surabaya
  • Riza Agung Firmansyah Institut Teknologi Adhi Tama Surabaya
  • Yuliyanto Agung Prabowo Institut Teknologi Adhi Tama Surabaya

Abstract

This paper presents a model to generate a 3D model of a room, where room mapping is very necessary to find out the existing real conditions, where this modeling will be applied to the rescue robot. To solve this problem, researchers made a breakthrough by creating a 3D room mapping system. The mapping system and 3D model making carried out in this study are to utilize the camera Kinect and Rviz on the ROS. The camera takes a picture of the area around it, the imagery results are processed in the ROS system, the processing carried out includes several nodes and topics in the ROS which later the signal results are sent and displayed on the Rviz ROS. From the results of the tests that have been carried out, the designed system can create a 3D model from the Kinect camera capture by utilizing the Rviz function on the ROS. From this model later every corner of the room can be mapped and modeled in 3D

References

V. N. Lu et al., ‘Service robots, customers and service employees: what can we learn from the academic literature and where are the gaps?’, J. Serv. Theory Pract., vol. 30, no. 3, pp. 361–391, Jan. 2020, doi: 10.1108/JSTP-04-2019-0088.

J. Luong et al., ‘Eversion and Retraction of a Soft Robot Towards the Exploration of Coral Reefs’, in 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft), Apr. 2019, pp. 801–807. doi: 10.1109/ROBOSOFT.2019.8722730.

A. Martins et al., ‘UX 1 system design - A robotic system for underwater mining exploration’, in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2018, pp. 1494–1500. doi: 10.1109/IROS.2018.8593999.

L. Zhi and M. Xuesong, ‘Navigation and Control System of Mobile Robot Based on ROS’, in 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Oct. 2018, pp. 368–372. doi: 10.1109/IAEAC.2018.8577901.

D. R. dos Santos, M. A. Basso, K. Khoshelham, E. de Oliveira, N. L. Pavan, and G. Vosselman, ‘Mapping Indoor Spaces by Adaptive Coarse-to-Fine Registration of RGB-D Data’, IEEE Geosci. Remote Sens. Lett., vol. 13, no. 2, pp. 262–266, Feb. 2016, doi: 10.1109/LGRS.2015.2508880.

J. Lee, S. Hwang, W. J. Kim, and S. Lee, ‘SAM-Net: LiDAR Depth Inpainting for 3D Static Map Generation’, IEEE Trans. Intell. Transp. Syst., pp. 1–16, 2021, doi: 10.1109/TITS.2021.3111046.

S. Muharom, ‘Automatics Detect and Shooter Robot Based on Object Detection Using Camera’, PRZEGLĄD ELEKTROTECHNICZNY, vol. 1, no. 1, pp. 52–56, Jan. 2022, doi: 10.15199/48.2022.01.07.

K. Koide, J. Miura, M. Yokozuka, S. Oishi, and A. Banno, ‘Interactive 3D Graph SLAM for Map Correction’, IEEE Robot. Autom. Lett., vol. 6, no. 1, pp. 40–47, Jan. 2021, doi: 10.1109/LRA.2020.3028828.

Y. Zheng, S. Chen, and H. Cheng, ‘Real-Time Cloud Visual Simultaneous Localization and Mapping for Indoor Service Robots’, IEEE Access, vol. 8, pp. 16816–16829, 2020, doi: 10.1109/ACCESS.2020.2966757.

T. Lee, C. Kim, and D. D. Cho, ‘A Monocular Vision Sensor-Based Efficient SLAM Method for Indoor Service Robots’, IEEE Trans. Ind. Electron., vol. 66, no. 1, pp. 318–328, Jan. 2019, doi: 10.1109/TIE.2018.2826471.

J. J. Leonard and H. F. Durrant-Whyte, ‘Simultaneous map building and localization for an autonomous mobile robot’, in Proceedings IROS ’91:IEEE/RSJ International Workshop on Intelligent Robots and Systems ’91, Nov. 1991, pp. 1442–1447 vol.3. doi: 10.1109/IROS.1991.174711.

M. Köseoğlu, O. M. Çelik, and Ö. Pektaş, ‘Design of an autonomous mobile robot based on ROS’, in 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Sep. 2017, pp. 1–5. doi: 10.1109/IDAP.2017.8090199.

J. Yi, J. Zhang, D. Song, and S. Jayasuriya, ‘IMU-based localization and slip estimation for skid-steered mobile robots’, in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2007, pp. 2845–2850. doi: 10.1109/IROS.2007.4399477.

S. Muharom, A. Rizkiawan, I. Masfufiah, R. A. Firmansyah, and Y. A. Prabowo, ‘Detection and Erasing Scribble Blackboard System Based on Hough-Transform Method Using Camera’, J. Phys. Conf. Ser., vol. 2117, no. 1, p. 012010, Nov. 2021, doi: 10.1088/1742-6596/2117/1/012010.

R. A. Firmansyah, I. K. Wicaksono, S. Muharom, Y. A. Prabowo, and A. Fahruzi, ‘Sorting Device Coding Print Quality Machine on Packing Box Prototype Utilizing Optical Character Recognition’, J. Phys. Conf. Ser., vol. 2117, no. 1, p. 012017, Nov. 2021, doi: 10.1088/1742-6596/2117/1/012017.

S. Muharom, I. Masfufiah, D. Purwanto, R. Mardiyanto, B. Prasetyo, and S. Asnawi, ‘Room Searching Robot Based on Door Detection and Room Number Recognition for Automatic Target Shooter Robot Application’, Proc. 1st Int. Conf. Electron. Biomed. Eng. Health Inform., pp. 43–54, 2021, doi: 10.1007/978-981-33-6926-9_4.

W. Deng et al., ‘Semantic RGB-D SLAM for Rescue Robot Navigation’, IEEE Access, vol. 8, pp. 221320–221329, 2020, doi: 10.1109/ACCESS.2020.3031867.

H. Wang, C. Wang, and L. Xie, ‘Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment’, IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 1715–1721, Apr. 2021, doi: 10.1109/LRA.2021.3059567.

F. Niroui, K. Zhang, Z. Kashino, and G. Nejat, ‘Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments’, IEEE Robot. Autom. Lett., vol. 4, no. 2, pp. 610–617, Apr. 2019, doi: 10.1109/LRA.2019.2891991.

S. Muharom, Tukadi, T. Odinanto, S. Fahmiah, and D. P. P. Siwi, ‘Design of Wheelchairs Robot Based on ATmega128 to People with Physical Disability’, IOP Conf. Ser. Mater. Sci. Eng., vol. 462, p. 012016, Jan. 2019, doi: 10.1088/1757-899X/462/1/012016.

R. Firmansyah, ‘Thermal Imaging-Based Body Temperature and Respiratory Frequency Measurement System for Security Robot’, PRZEGLĄD ELEKTROTECHNICZNY, vol. 1, no. 6, pp. 128–132, Jun. 2022, doi: 10.15199/48.2022.06.23.

M. Aggravi, A. A. S. Elsherif, P. R. Giordano, and C. Pacchierotti, ‘Haptic-Enabled Decentralized Control of a Heterogeneous Human-Robot Team for Search and Rescue in Partially-Known Environments’, IEEE Robot. Autom. Lett., vol. 6, no. 3, pp. 4843–4850, Jul. 2021, doi: 10.1109/LRA.2021.3067859.

C. Wang, W. Chi, Y. Sun, and M. Q.-H. Meng, ‘Autonomous Robotic Exploration by Incremental Road Map Construction’, IEEE Trans. Autom. Sci. Eng., vol. 16, no. 4, pp. 1720–1731, Oct. 2019, doi: 10.1109/TASE.2019.2894748.

B. Guo, H. Dai, Z. Li, and W. Huang, ‘Efficient Planar Surface-Based 3D Mapping Method for Mobile Robots Using Stereo Vision’, IEEE Access, vol. 7, pp. 73593–73601, 2019, doi: 10.1109/ACCESS.2019.2920511.

K. Ohno, T. Nomura, and S. Tadokoro, ‘Real-Time Robot Trajectory Estimation and 3D Map Construction using 3D Camera’, in 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct. 2006, pp. 5279–5285. doi: 10.1109/IROS.2006.282027.

H. Wang, C. Zhang, Y. Song, B. Pang, and G. Zhang, ‘Three-Dimensional Reconstruction Based on Visual SLAM of Mobile Robot in Search and Rescue Disaster Scenarios’, Robotica, vol. 38, no. 2, pp. 350–373, Feb. 2020, doi: 10.1017/S0263574719000675.

H. Lu, S. Yang, M. Zhao, and S. Cheng, ‘Multi-Robot Indoor Environment Map Building Based on Multi-Stage Optimization Method’, Complex Syst. Model. Simul., vol. 1, no. 2, pp. 145–161, Jun. 2021, doi: 10.23919/CSMS.2021.0011.

W. Ali, P. Liu, R. Ying, and Z. Gong, ‘A Feature Based Laser SLAM Using Rasterized Images of 3D Point Cloud’, IEEE Sens. J., vol. 21, no. 21, pp. 24422–24430, Nov. 2021, doi: 10.1109/JSEN.2021.3113304

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Published

2024-04-19

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Section

Control, Automation and Robotics