aiqbench analysis
This research advances autonomous underwater vehicle navigation by leveraging precise inertial heading data to linearize the initialization phase of range-aided SLAM systems. By solving transponder and robot positions through a generalized trust region subproblem followed by least-squares estimation, the method effectively decouples nonlinear optimization challenges in deep-sea environments. The approach offers a robust pathway for improving localization accuracy where traditional GPS signals are unavailable.
Range-Aided SLAM Initialization Exploiting Accurate Heading Information
arXiv:2609.24846v1 Announce Type: new Abstract: 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).
Abstract: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).
Robotics (cs.RO)
arXiv:2609.24846 [cs.RO]
(or arXiv:2609.24846v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.24846 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 281 words
