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Descriptor-Length Reduction Using Low-Variance Filter for Visual Odometry

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Proceedings of 3rd International Conference on Computer Vision and Image Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1024))

Abstract

Visual odometry is a popular technique used to estimate motion in GPS-challenged environment, whose accuracy depends on the features extracted from the images. In past attempts to improved feature distinctiveness, these features have become complex and lengthier, requiring more storage space and computational power for matching. In this paper, an attempt is made toward reducing the length of these feature descriptors while maintaining a similar accuracy in pose estimation. Elimination of feature indices based on variance analysis on feature column sets is proposed and experimented in this paper. The features with reduced descriptor length are applied over the 3D-2D visual odometry pipeline and experimented on KITTI dataset for evaluating its efficacy. The proposed scheme of variance-based descriptor length reduction is found to reduce the overall time taken by the motion estimation framework while estimating the transformation with similar accuracy as that with full-length feature vector.

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Acknowledgements

This research has been supported by DRDO—Aeronautical Research & Development Board through grant-in-aid project on design and development of visual odometry System.

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Authors and Affiliations

  1. Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    Shrijay S. Kalambe & Elizabeth Rufus

  2. CSIR-Central Scientific Instruments Organization, Sector 30, Chandigarh, India

    Vinod Karar & Shashi Poddar

Authors
  1. Shrijay S. Kalambe
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  2. Elizabeth Rufus
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  3. Vinod Karar
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  4. Shashi Poddar
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Corresponding author

Correspondence to Shashi Poddar .

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Editors and Affiliations

  1. Techno India University, Kolkata, India

    Bidyut B. Chaudhuri

  2. Division of Advanced Information Technology and Computer Science, Tokyo University of Agriculture and Technology, Koganei-shi, Tokyo, Japan

    Masaki Nakagawa

  3. Department of Computer Science, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya Pradesh, India

    Pritee Khanna

  4. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Kumar

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Kalambe, S.S., Rufus, E., Karar, V., Poddar, S. (2020). Descriptor-Length Reduction Using Low-Variance Filter for Visual Odometry. In: Chaudhuri, B., Nakagawa, M., Khanna, P., Kumar, S. (eds) Proceedings of 3rd International Conference on Computer Vision and Image Processing. Advances in Intelligent Systems and Computing, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-32-9291-8_1

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  • DOI: https://doi.org/10.1007/978-981-32-9291-8_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-32-9290-1

  • Online ISBN: 978-981-32-9291-8

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