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]East China Univ Sci & Technol, Dept Automat, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China

年份：2025

卷号：10

期号：6

起止页码：5313

外文期刊名：IEEE ROBOTICS AND AUTOMATION LETTERS

收录：;EI(收录号：20251618243160);WOS:【SCI-EXPANDED(收录号:WOS:001473122300007)】；

基金：The work of Chao Yang was supported by NSFC under Grant 62336005. The work of Junfeng Wu was supported in part by NSFC under Grant 62273288 and Grant 62336005, in part by the Shenzhen Science and Technology Program under Grant JCYJ20220818103000001, and in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2024A151524009.

语种：英文

外文关键词：Sonar; Sonar measurements; Translation; Azimuth; Pose estimation; Noise; Estimation; Vectors; Three-dimensional displays; Sensors; Underwater robots; 2D forward-looking sonar; pose estimation; acoustic-n-point problem

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 6(degrees)-of-freedom (DoF) 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-of-the-art (SOTA) methods, BESTAnP is over ten times faster and features real-time capacity in resource-constrained platforms while exhibiting comparable accuracy. Moreover, we embed BESTAnP into a single sonar-based odometry which shows its effectiveness for trajectory estimation.