Abstract
This paper presents a novel approach to state estimation based on particle filter dealing with measurement data effected by non-Gaussian, multimodal noise. The implementation focusses on autonomous underwater vehicles (AUVs) utilizing data of a magnetic compass and a mechanical scanning sonar for spatial navigation. Nowadays, particle filter approaches often require complicated feature extraction methods culminating in semantic interpretation of the data. This is not suitable for low-cost and low-weight AUVs, because these steps require high computational power. Therefore, efficient CPUs and higher power delivery are required. To test the novel approach, the algorithm is simulated in different scenarios with different parameters. Additionally, the filter is applied to real environment data. Finally, the performance is tested and evaluated by several methods. We demonstrate the computational efficiency and superiority of our method over other approaches through simulations.
Highlights
MOTIVATION In the last few decades the research of underwater environments supported by robots like remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs) has increased significantly due to the off-shore industry, monitoring of flora and fauna, geological mapping and many more applications
While ROVs still need an operator to control the vehicle, AUVs are capable of fulfilling a mission autonomously
To find suitable parameters and evaluate the new approaches an AUV simulation framework was built in MATLAB
Summary
A. MOTIVATION In the last few decades the research of underwater environments supported by robots like remotely operated vehicles (ROVs) and AUVs has increased significantly due to the off-shore industry, monitoring of flora and fauna, geological mapping and many more applications. While ROVs still need an operator to control the vehicle, AUVs are capable of fulfilling a mission autonomously. AUVs require robust localization and navigation methods to deliver reliable data. Determined by the requirements, AUVs come in different sizes and capabilities. We are focussing on so called low-cost and low-weight AUVs, which implies sensors with less precise data and limited processing power. This makes the possibilities for navigation and localization difficult. Methods like long baseline (LBL) or short baseline (SBL) are very precise, but require a base station on the surface or previously deployed
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