Accurate heading improves underwater vehicle localization with range measurements
Range-Aided SLAM Initialization Exploiting Accurate Heading Information
Robotics
Summary
Figuring out where underwater robots and their landmarks are is tricky, especially at the start. This paper shows a new way to use very accurate compass-like direction info to help find these positions from distance measurements. The method first guesses landmark spots using math that handles tricky measurements, then uses those guesses and heading info to find exact positions in a straightforward way. By combining these steps, the authors help underwater robots navigate and map better from the beginning. They tested it using real underwater robot data with good results.
What this means in practice
- •For auv navigation teams: Improve initial localization and mapping accuracy of underwater vehicles using accurate heading and range data.
- •For marine robotics developers: Integrate heading-aided initialization methods to enhance underwater robot positioning reliability in mapping systems.
Authors
Isabel Lougheed, James Richard Forbes
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).