Semi-dense visual odometry for RGB-D cameras using approximate nearest neighbour fields

Yi Zhou, Laurent Kneip, Hongdong Li

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    12 Citations (Scopus)

    Abstract

    This paper presents a robust and efficient semidense visual odometry solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline which estimates the pose of the sensor by registering the projection of a 3D semidense map of a reference frame with the 2D semi-dense region extracted in the current frame. The processing is speeded up by efficiently implemented approximate nearest neighbour fields under the Euclidean distance criterion, which permits the use of compact Gauss-Newton updates in the optimization. The registration is formulated as a maximum a posterior problem to deal with outliers and sensor noise, and the equivalent weighted least squares problem is consequently solved by iteratively reweighted least squares method. A variety of robust weight functions are tested and the optimum is determined based on the probabilistic characteristics of the sensor model. Extensive evaluation on publicly available RGB-D datasets shows that the proposed method predominantly outperforms existing state-of-the-art methods.

    Original languageEnglish
    Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages6261-6268
    Number of pages8
    ISBN (Electronic)9781509046331
    DOIs
    Publication statusPublished - 21 Jul 2017
    Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
    Duration: 29 May 20173 Jun 2017

    Publication series

    NameProceedings - IEEE International Conference on Robotics and Automation
    Volume0
    ISSN (Print)1050-4729

    Conference

    Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
    Country/TerritorySingapore
    CitySingapore
    Period29/05/173/06/17

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