Document Type
Article
Publication Date
Summer 2023
Abstract
One of the fundamental problems of robotics and navigation is the estimation of the relative pose of an external object with respect to the observer. A common method for computing the relative pose is the iterative closest point (ICP) algorithm, where a reference point cloud of a known object is registered against a sensed point cloud to determine relative pose. To use this computed pose information in downstream processing algorithms, it is necessary to estimate the uncertainty of the ICP output, typically represented as a covariance matrix. In this paper, a novel method for estimating uncertainty from sensed data is introduced.
Source Publication
NAVIGATION: Journal of the Institute of Navigation
Recommended Citation
Yuan, H. R., Taylor, C. N., & Nykl, S. L. (2023). Accurate covariance estimation for pose data from iterative closest point algorithm. NAVIGATION, 70(2). https://doi.org/10.33012/navi.562
Comments
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0)
Shared as a link in accordance with guidance at Sherpa for the source journal, NAVIGATION.
This work is adapted from a 2021 ION conference paper. https://doi.org/10.33012/2021.17866. The conference paper version is not open access.