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Room Mapping and Robot Localization Using RGB-D Camera and IMU Sensor

Published: 13 February 2022 Publication History

Abstract

Room mapping and localization of mobile robots is an important and fundamental problem in robotics research. Environmental perception and identification of the robot’s position and orientation to a global coordinate are basic prerequisites for autonomous navigation on mobile robots. In situations where autonomous mobile robots must operate in an unknown environment, localization and mapping are required simultaneously. Thus, simultaneous localization and mapping (SLAM) on mobile robots in unknown environments has become a topic that is widely discussed due to its important theoretical value and possible application. The SLAM is considered the key to realizing the autonomous mobile robot. This also applies to domestic service robots (DSR) that operate in a dynamic environment. In order to accomplish its duties in this dynamic environment, DSR must be able to recognize the environment and the various changes that occur around it. In this paper, we develop the domestic service robots (DSR) and implement SLAM that is based on visual information captured from the RGB-D (i.e., color and depth) camera combined with an inertial measurement unit (IMU). To test the developed systems, we have conducted several experiments. As the preliminary study, we achieved good results.

References

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[1] H. Durrant-Whyte, and T. Bailey, “Simultaneous Localization and Mapping: Part I,” IEEE Robotics & Automation Magazine, vol. 13, no. 2, 2006, pp. 99–110.
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[2] T. Bailey, and H. Durrant-Whyte, “Simultaneous Localization and Mapping (SLAM): Part II,” IEEE Robotics & Automation Magazine, vol. 13, no. 3, 2006, pp. 108–117.
[3]
[3] F. Endres, J. Hess, J. Sturm, D. Cremers, and W. Burgard, “3D Mapping with an RGBD Camera,” IEEE Transactions on Robotics, vol. 30, no. 1, 2014, pp. 268–274.
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[4] E. Kagan, N. Shvalb, and I. Ben-Gal, “Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming,” 2020, Wiley.
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[5] Jetson Nano Developer Kit: https://developer.nvidia.com/embedded/jetson-nano-developer-kit.
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[6] Intel RealSense Depth Camera D435i: https://www.intelrealsense.com/ depth-camera-d435i/.
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[7] M. Labbe, and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in Journal of Field Robotics, vol. 36, no. 2, 2019, pp. 416–446.
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[8] M. Labbe, and F. Michaud, “Appearance-Based Loop Closure Detection for Online Large-Scale and Long-Term Operation,” in IEEE Transactions on Robotics, vol. 29, no. 3, 2013, pp. 734–745.
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[9] M. Labbe, and F. Michaud, “Memory management for Real-time Appearance-based Loop Closure Detection,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2011, pp. 1271–1276.
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[11] Arduino Uno: https://www.arduino.cc/en/Guide/ArduinoUno
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[12] Arduino Nano: https://www.arduino.cc/en/Guide/ArduinoNano
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[13] S. O. H. Madgwick, A. J. L. Harrison, and R. Vaidyanathan, “Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm,” IEEE International Conference on Rehabilitation Robotics, 2011, pp. 1–7.

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cover image ACM Other conferences
IC3INA '21: Proceedings of the 2021 International Conference on Computer, Control, Informatics and Its Applications
October 2021
204 pages
ISBN:9781450385244
DOI:10.1145/3489088
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 13 February 2022

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Author Tags

  1. IMU sensor
  2. RGB-D camera
  3. SLAM
  4. robot localization
  5. room mapping

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