Optics and Precision Engineering, Volume. 31, Issue 5, 581(2023)

Test and verification of rut recognition on complex ground

Long XUE1... Lan LI1, Zhaolong DANG2, Baichao CHEN2, Meng ZOU3 and Jing LI1,* |Show fewer author(s)
Author Affiliations
  • 1School of Engineering, Jiangxi Agricultural University, Nanchang330045, China
  • 2Institute of Spacecraft System Engineering,China Academy of Space Technology, Beijing100094,China
  • 3Key Laboratory for Bionics Engineering of Education Ministry, Jilin University, Changchun10022, China
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    The geomorphological characterization of Mars is complex. Therefore, to ensure the safe driving of rover, it is essential to understand the surface state around the rover through images captured by on-board digital cameras. The images are first preprocessed using stereo vision to create an aerial view, which is then divided into equal-sized blocks. Next, calibration and prediction datasets are created, containing 315 and 135 datapoints, respectively. Based on these datasets, a neural network model is developed. Finally, the image is classified using the resulting classification model to identify the region of interest. The results of the classification show that its accuracy on the calibration and prediction datasets using ResNet50 is 75.56% and 81.48%, respectively. This method can help researchers characterize the surface types around UGVs and identify the regions of interest that may provide more valuable information from the images. It can also be used for traversability prediction, risk assessment, and automatic path planning.

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    Long XUE, Lan LI, Zhaolong DANG, Baichao CHEN, Meng ZOU, Jing LI. Test and verification of rut recognition on complex ground[J]. Optics and Precision Engineering, 2023, 31(5): 581

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    Paper Information

    Category: Terramechanics

    Received: Jun. 6, 2022

    Accepted: --

    Published Online: Apr. 4, 2023

    The Author Email: LI Jing (lijing3815@163.com)

    DOI:10.37188/OPE.20233105.0581

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