文献类型：期刊文献

英文题名：BESTAnP: Bi-Step Efficient and Statistically Optimal Estimator for Acoustic-n-Point Problem

作者：Sheng, Wenliang[1]; Zhao, Hongxu[2]; Chen, Lingpeng[2]; Zeng, Guangyang[2]; Shao, Yunling[2]; Hong, Yuze[2]; Yang, Chao[1]; Hong, Ziyang[2]; Wu, Junfeng[2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Dept. of Automation, East China University of Science and Technology, Shanghai, China; [2] The School of Data Science, Chinese University of Hong Kong, Shenzhen, China

年份：2024

外文期刊名：arXiv

收录：EI(收录号：20240509425)

语种：英文

外文关键词：Constrained optimization - Digital arithmetic - Higher order statistics - Statistics - Underwater acoustics

摘要：We consider the acoustic-n-point (AnP) problem, which estimates the pose of a 2D forward-looking sonar (FLS) according to n 3D-2D point correspondences. We explore the nature of the measured partial spherical coordinates and reveal their inherent relationships to translation and orientation. Based on this, we propose a bi-step efficient and statistically optimal AnP (BESTAnP) algorithm that decouples the estimation of translation and orientation. Specifically, in the first step, the translation estimation is formulated as the range-based localization problem based on distance-only measurements. In the second step, the rotation is estimated via eigendecomposition based on azimuth-only measurements and the estimated translation. BESTAnP is the first AnP algorithm that gives a closed-form solution for the full six-degree pose. In addition, we conduct bias elimination for BESTAnP such that it owns the statistical property of consistency. Through simulation and real-world experiments, we demonstrate that compared with the state-ofthe- art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, for the first time, we embed BESTAnP into a sonar-based odometry which shows its effectiveness for trajectory estimation. Copyright ? 2024, The Authors. All rights reserved.