Research Repository

Improving Localization Accuracy for an Underwater Robot with a Slow-Sampling Sonar Through Graph Optimization

Chen, L and Wang, S and Hu, H and Gu, D and Liao, L (2015) 'Improving Localization Accuracy for an Underwater Robot with a Slow-Sampling Sonar Through Graph Optimization.' IEEE Sensors Journal, 15 (9). 5024 - 5035. ISSN 1530-437X

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This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 31 Aug 2015 13:57
Last Modified: 30 Mar 2021 23:15

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