发表机构
McGill University（麦吉尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种利用高精度航向信息的两步RA-SLAM初始化方法，通过GTRS和线性最小二乘求解应答器与AUV位置，并在真实AUV数据集上验证有效性。
AI 中文摘要
本文提出了一种用于距离辅助同时定位与建图（RA-SLAM）的新型初始化方法。一般SLAM问题具有众所周知的分离结构，即在已知机器人航向的情况下，地标和机器人位置可以线性方式求解。本文考虑高精度航向信息可用的情况（这是自主水下航行器（AUV）导航的典型特征），以求解距离应答器和机器人位置。所提出的方法包括两个步骤。首先，利用广义信任域子问题（GTRS），在给定非线性距离测量的情况下，求解应答器相对于AUV的位置。其次，利用相对应答器位置和已知的AUV航向，通过求解线性最小二乘问题来估计应答器和AUV的位置。这些应答器和AUV位置估计，结合高精度航向信息，为一般非线性RA-SLAM问题提供了一种可靠的初始化方法。所提出方法的有效性在真实AUV数据集上进行了测试，该数据集提供了长基线（LBL）距离测量，以及由惯性导航系统（INS）提供的高精度航向信息。
英文摘要
This paper presents a novel initialization method for range-aided simultaneous localization and mapping (RA-SLAM). The general SLAM problem has a well-known separable structure where landmark and robot positions can be solved for in a linear fashion given known robot headings. This paper considers the case where highly accurate heading information is available, which is typical of autonomous underwater vehicle (AUV) navigation, to solve for the range transponder and robot positions. The proposed approach consists of two steps. First, using a generalized trust region subproblem (GTRS), the positions of the transponders relative to the AUV are solved for given the nonlinear range measurements. Second, the relative transponder positions and the known heading of the AUV are used to estimate the transponder and AUV positions by solving a linear least-squares problem. These transponder and AUV position estimates, combined with the highly accurate heading information, provide a reliable initialization method for the general nonlinear RA-SLAM problem. The effectiveness of the proposed approach is tested on a real-world AUV dataset where long baseline (LBL) range measurements are provided in concert with highly accurate heading information provided by an inertial navigation system (INS).
Comments7 pages, 5 figures, accepted to OCEANS 2026 MTS/IEEE Conference and Exhibition
