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Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization

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Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11845))

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Abstract

Lidar-Monocular Visual Odometry (LIMO), an odometry estimation algorithm, combines camera and LIght Detection And Ranging sensor (LIDAR) for visual localization by tracking features from camera images and LIDAR measurements. LIMO then estimates the motion using Bundle Adjustment based on robust key frames. LIMO uses semantic labelling and weights of the vegetation landmarks for rejecting outliers. A drawback of LIMO as well as many other odometry estimation algorithms is that it has many parameters that need to be manually adjusted according to dynamic changes in the environment in order to decrease translational errors. In this paper, we present and argue the use of Genetic Algorithm (GA) to optimize parameters with reference to LIMO and maximize LIMO’s localization and motion estimation performance. We evaluate our approach on the well-known KITTI odometry dataset and show that the GA helps LIMO to reduce translation error in different datasets.

This material is based upon work supported by the National Aeronautics and Space Administration (NASA) Grant No. NNX15AI02H issued through the NVSGC-RI program under sub-award No. 19–21, and the RID program under sub-award No. 19–29, and the NVSGC-CD program under sub-award No. 18–54. This work is also partially supported by the Office of Naval Research under Grant N00014-17-1-2558.

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Authors and Affiliations

  1. University of Nevada, Reno, NV, 89557, USA

    Adarsh Sehgal, Ashutosh Singandhupe, Hung Manh La, Alireza Tavakkoli & Sushil J. Louis

Authors
  1. Adarsh Sehgal
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  2. Ashutosh Singandhupe
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  3. Hung Manh La
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  4. Alireza Tavakkoli
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  5. Sushil J. Louis
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Corresponding author

Correspondence to Hung Manh La .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Sehgal, A., Singandhupe, A., La, H.M., Tavakkoli, A., Louis, S.J. (2019). Lidar-Monocular Visual Odometry with Genetic Algorithm for Parameter Optimization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_29

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  • DOI: https://doi.org/10.1007/978-3-030-33723-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33722-3

  • Online ISBN: 978-3-030-33723-0

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