@inproceedings{aa071d1f72d9469e9a7b09cdb303483f,
title = "Rotational Alignment of IMU-camera Systems with 1-Point RANSAC",
abstract = "In this paper we present a minimal solution for the rotational alignment of IMU-camera systems based on a homography formulation. The image correspondences between two views are related by homography when the motion of the camera can be effectively approximated as a pure rotation. By exploiting the rotational angles of the features obtained by e.g. the SIFT detector, we compute the rotational alignment of IMU-camera systems with only 1 feature correspondence. The novel minimal case solution allows us to cope with feature mismatches efficiently and robustly within a random sample consensus (RANSAC) scheme. Our method is evaluated on both synthetic and real scene data, demonstrating that our method is suited for the rotational alignment of IMU-camera systems.",
author = "Guan Banglei and Ang Su and Zhang Li and Friedrich Fraundorfer",
year = "2019",
doi = "10.1007/978-3-030-31726-3_15",
language = "English",
isbn = "978-3-030-31725-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
number = "11859",
pages = "172--183",
editor = "Z. Lin",
booktitle = "Pattern Recognition and Computer Vision",
note = "Chines Conference on Pattern Recognition and Computer Vision : PRCV 2019, PRCV 2019 ; Conference date: 08-11-2019 Through 09-11-2019",
}