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Visual SLAM for Texture-Less Environment

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Autonomous Driving Perception

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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Abstract

Recent researches on vision-based self-localization have catalyzed versatile and reliable real-time Visual Simultaneous Localization and Mapping (VSLAM) systems. However, retrieving ground truth, estimating calibration parameters and annotating useful labels all require cumbersome human labor. Moreover, there are lots of object instances in the environments while traditional mapping modules can only estimate 3D information of isolated sparse or semi-dense feature points. To meet the gap between the above requirements, we present a VSLAM method based on a synthetic dataset which can effectively utilize texture-less object instances. We also propose several new evaluation criteria that can fully take advantage of ground truth and annotations from synthetic datasets. The proposed Visual SLAM method includes newly designed feature extraction, matching, localization and mapping modules, which jointly use object features and point features to estimate camera 6-Degrees Of Freedom (6-DOF) poses and do richer map construction. Experiments are conducted using the proposed datasets and criteria with several state-of-the-art VSLAM methods to demonstrate the functionality of our datasets. Owing to the object feature fusion in the co-visibility graph, it can conducts scale aware bundle adjustments to reduce accumulated errors. The advantages of proposed Visual SLAM method are demonstrated through experiments conducted both on synthetic datasets and real-world datasets.

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Notes

  1. 1.

    Many works that claim themselves as “Visual Odometry” possess many features similar to SLAM system; therefore we refer both VO and Visual SLAM as “VSLAM” in the following paper.

  2. 2.

    https://www.unrealengine.com/.

  3. 3.

    https://unity3d.com/.

  4. 4.

    https://rendering.ru/madcar.html.

  5. 5.

    https://www.autodesk.com/products/3ds-max/overview.

  6. 6.

    https://www.chaosgroup.com/vray/3ds-max.

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Authors and Affiliations

  1. College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, People’s Republic of China

    Yanchao Dong, Yuhao Liu & Sixiong Xu

Authors
  1. Yanchao Dong
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  2. Yuhao Liu
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  3. Sixiong Xu
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Correspondence to Sixiong Xu .

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Editors and Affiliations

  1. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

    Rui Fan

  2. Control Science & Engineering, Tongji University and Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China

    Sicen Guo

  3. Electrical & Electronic Engineering, University of Bristol, Bristol, UK

    Mohammud Junaid Bocus

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Dong, Y., Liu, Y., Xu, S. (2023). Visual SLAM for Texture-Less Environment. In: Fan, R., Guo, S., Bocus, M.J. (eds) Autonomous Driving Perception. Advances in Computer Vision and Pattern Recognition. Springer, Singapore. https://doi.org/10.1007/978-981-99-4287-9_8

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  • DOI: https://doi.org/10.1007/978-981-99-4287-9_8

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