Drift and scale ambiguity are two main issues that reduce localization accuracy in monocular visual odometry (MVO). It is necessary to propose a unified model to represent these measurement uncertainties. In this chapter, we present a geometric map‐assisted localization approach for mobile robots equipped with MVO. We model the measurement of MVO as a group of particles that obey uniform‐Gaussian or Gaussian‐Gaussian distribution, depending on measurement noise assumptions. The proposed model covers measurement uncertainties including scale ambiguity and measurement randomness. The saliency of each particle can be obtained from the distribution to indicate the raw measurement certainty of MVO. Geometric map‐assisted shape matching is implemented as the measurement model to assign consistency to the particles generated from the distribution. Both saliency and consistency are considered in particle weight determination. Furthermore, based on the statistical properties of the probability distribution, a parameter estimation scheme is proposed to narrow down the scale ambiguity of MVO while resampling particles. Experiments with the KITTI dataset have demonstrated that the proposed approach greatly enhances positioning accuracy with the help of off‐the‐shelf digital maps.


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    Titel :

    Road‐Constrained Localization Using Cloud Models


    Beteiligte:
    Liu, Xinghua (Autor:in) / Jiang, Rui (Autor:in) / Chen, Badong (Autor:in) / Sam Ge, Shuzhi (Autor:in)


    Erscheinungsdatum :

    27.09.2022


    Format / Umfang :

    27 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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