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Vision-IMU Based Obstacle Detection Method

  • Yi Xu
  • Song Gao
  • Shiwu Li
  • Derong Tan
  • Dong Guo
  • Yuqiong Wang
  • Qiang Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

Obstacles’ accurate classification is the first step in traditional obstacle detection methods, and the step causes the problem of high time and space complexity. In this paper, an obstacle detection method based on the principle of pinhole imaging is proposed to solve the problem. The monocular camera and inertial measurement unit are used as the basic sensing units in proposed method. The obstacle detection steps and indoor experiments are shown to expound the detection process of the Vision-IMU based obstacle detection method. The Vision-IMU based obstacle detection method and Adaboost cascade detection method are used to detect obstacles in indoor experiments, and the Producer’s Accuracy, the User’s Accuracy, the Overall Accuracy, and κ are used as evaluating indicators to compare test results, and the results show that the Vision-IMU based obstacle detection method has higher accuracy. The processing time of the Vision-IMU based obstacle detection method and Adaboost cascade detection method are compared, and it is shown that the Vision-IMU based obstacle detection method has faster processing speed.

Keywords

Monocular vision Inertial measurement unit Obstacle detection Pinhole imaging 

Notes

Acknowledgements

Research was supported by Key Projects of National Key R & D Plan (2016YFD0701101), China Postdoctoral Science Foundation (2018M632696), Changbai Mountain Scholars Program (440020031167), National Natural Science Foundation of China (51508315), Natural Science Foundation of Shandong Province (ZR2016EL19, ZR2018PEE016, ZR2018LF009).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yi Xu
    • 1
  • Song Gao
    • 1
    • 3
  • Shiwu Li
    • 2
  • Derong Tan
    • 1
  • Dong Guo
    • 1
  • Yuqiong Wang
    • 1
  • Qiang Chen
    • 2
    • 4
  1. 1.School of Transportation and Vehicle EngineeringShandong University of TechnologyZiboChina
  2. 2.School of TransportationJilin UniversityChangchunChina
  3. 3.Collaborative Innovation Center for New Energy Vehicle of Shandong UniversitiesZiboChina
  4. 4.School of Automotive and TransportationTianjin University of Technology and EducationTianjinChina

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