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Real-time obstacle detection in a darkroom using a monocular camera and a line laser

Published: 01 November 2022 Publication History

Abstract

When a disaster strikes, one of the highest priorities is to save the lives of the victims. The proportion of victims that rescuers can save is strongly related to how quickly rescue efforts can begin. Therefore, early detection of disaster victims is very important. However, significant risks are involved in rescue operations immediately after a disaster. In fact, in the Great East Japan Earthquake, approximately 250 firefighters died while rescuing victims. Under such circumstances, rapid and safe rescue operations are needed at disaster sites. For this purpose, it is important to improve the technology of disaster relief robots. In this paper, we propose an algorithm to measure the linear distance to an obstacle in real time using only a line laser and a monocular camera. This approach allows the use of a camera to obtain more information than the one-dimensional information such as that obtained by ultrasonic sensors. Moreover, this method of obstacle detection for disaster rescue robots is smaller and more durable than large measurement systems such as LiDAR that have been used in the past. In addition, since only one camera is used, the processing cost is low and the processing equipment is expected to be small. The proposed method’s effectiveness is indicated by comparing the distance measured from the image processing results in a dynamic environment with the actual distance between the obstacle and a crawler robot by having the robot move straight toward to an obstacle.

References

[1]
CRED International disaster database (EM-DAT),available from https://www.emdat.be/,(accessed on 20 May, 2022)
[2]
Cabinet Office, Government of Japan, Disaster Manegement in Japan (2021)
[3]
Fire and Disaster Management Agency Great East Japan Earthquake Damage Report No. 145
[4]
Suetsugu M, Yang S, Kyushu SS (2019) Institute of TechnologyMobile robot using line laser and camera Obstacle detection and shape recognition method, Proceedings of IIAE Annual Conference 2019
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Francisco AX, da Mota MXR, Joel JPC, Rodrigues VHC, de Albuquerque A, and de Alexandria AR Localization and navigation for autonomous mobile robots using petri nets in indoor environments IEEE Access 2018 6 31665-31676
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Siegwart R and Nourbakhsh IR Introduction to autonomous mobile robots 2004 Cambridge MIT Press
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Garnett N, Silberstein S, Oron S, Fetaya E, Verner U, Ayash A, Goldner V, Cohen R, Horn K, Levi D (2017) Advanced Technical Center Israel, General Motors R & D, Hamada 7, Herzlyia, Israel, Real-time category-based and general obstacle detection for autonomous driving, Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp.198-205
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Chen H-C Monocular vision-based obstacle detection and avoidance for a multicopter IEEE Access 2019 7 167869-167883
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Gibbs G and Jia H Obstacle detection with ultrasonic sensors and signal analysis metrics Transp Res Proc 2017 28 173-182
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Christian H, Lionel H, Lee GH, Fraundorfer F, Furgale P, Sattler T, and Pollefeys M 3D visual perception for self-driving cars using a multi-camera system: Calibration, mapping Image Vis Comput 2017 68 14-27
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Cited By

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  • (2024)Obstacle feature point detection method for real-time processing by monocular camera and line laserArtificial Life and Robotics10.1007/s10015-024-00965-429:4(438-448)Online publication date: 1-Nov-2024

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          Information & Contributors

          Information

          Published In

          cover image Artificial Life and Robotics
          Artificial Life and Robotics  Volume 27, Issue 4
          Nov 2022
          272 pages

          Publisher

          Springer-Verlag

          Berlin, Heidelberg

          Publication History

          Published: 01 November 2022
          Accepted: 03 August 2022
          Received: 14 April 2022

          Author Tags

          1. Robot vision and image processing
          2. Mobile robots
          3. Motion planning and navigation
          4. Obstacle detection

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          • (2024)Obstacle feature point detection method for real-time processing by monocular camera and line laserArtificial Life and Robotics10.1007/s10015-024-00965-429:4(438-448)Online publication date: 1-Nov-2024

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