Towards comparison of underwater SLAM methods: An open dataset collection

In this paper, we present an open collection of simulated datasets produced using the Underwater Simulator (UWSim). These datasets contain several trajectories in simulated scenarios with various levels of turbidity. Also, several sensor to estimate the robot displacement are available. The ground truth is available by using Global Positioning System data. Those information can be used to analyse and to perform Simultaneous Localization and Mapping (SLAM). A comparative study between a bio-inspired approach and one of the most adopted algorithm in SLAM, the Extended Kalman Filter (EKF), is produced using the datasets.

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