Point-to-hyperplane ICP: fusing different metric measurements for pose estimation

Abstract : The objective of this article is to provide a generalized framework of a novel method that investigates the problem of combining and fusing different types of measurements for pose estimation. The proposed method allows to jointly minimize the different metric errors as a single measurement vector in n-dimensions without requiring a scaling factor to tune their importance. This paper is an extended version of previous works that introduced the Point-to-hyperplane ICP approach. In this approach an increased convergence domain and a faster alignment was demonstrated by considering a 4-dimensional measurement vector (3D Euclidean points + Intensity). The method has the advantages of the classic Point-to-plane ICP method, but extends this to higher dimensions. For demonstration purposes, this paper will focus on a RGB-D sensor that provides color and depth measurements simultaneously and an optimal error in higher dimensions will be minimized from this. Results on both, simulated and real environments will be provided and the performance of the proposed method will be carried on real-time visual SLAM.
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https://hal.archives-ouvertes.fr/hal-02061500
Contributor : Andrew Comport <>
Submitted on : Friday, March 8, 2019 - 10:51:59 AM
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Fernando Ireta Munoz, Andrew I. Comport. Point-to-hyperplane ICP: fusing different metric measurements for pose estimation. Advanced Robotics, Taylor & Francis, 2018, 32 (4), pp.161-175. ⟨10.1080/01691864.2018.1434013⟩. ⟨hal-02061500⟩

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