Toward robust localization and mapping for shallow water ROV inspections

We propose a portfolio of filtering methods for a remotely-operated vehicle (ROV) that relies on a compass, gyro, depth sensor and ultra-short baseline (USBL) positioning system for localization in shallow-water environments. Our goal is to maintain an accurate state estimate that will be suitable as a basis for decision-making in the course of carrying out an autonomous underwater inspection. We employ Kalman filters in conjunction with an outlier filter to produce a reliable estimate in the presence of noisy and spurious data. Our localization result is then used as a basis for 3D occupancy mapping, in which further filtering and inference techniques are applied to remove false returns from the ROV´s scanning sonar. Localization results from two field deployments of the ROV are given.