Robotics: Science and Systems XVIII

KernelGPA: A Deformable SLAM Back-end

Fang Bai, Adrien Bartoli


Simultaneous localization and mapping (SLAM) in the deformable environment has encountered several barricades. One of them is the lack of a global registration technique. Thus current SLAM systems heavily rely on template based methods. We propose KernelGPA, a novel global registration technique to bridge the gap. We define nonrigid transformations using a kernel method, and show that the principal axes of the map can be solved globally in closed-form, up to a global scale ambiguity along each axis. We propose to solve both the global scale ambiguity and rigid poses in a unified optimization framework, yielding a cost that can be readily incorporated in sensor fusion frameworks. We demonstrate the registration performance of KernelGPA using various datasets, with a special focus on computerized tomography (CT) registration. We release our code and data to foster future research in this direction.



    AUTHOR    = {Fang Bai AND Adrien Bartoli}, 
    TITLE     = {{KernelGPA: A Deformable SLAM Back-end}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2022}, 
    ADDRESS   = {New York City, NY, USA}, 
    MONTH     = {June}, 
    DOI       = {10.15607/RSS.2022.XVIII.002}