Comparison of feature detection and outlier removal strategies in a mono visual odometry algorithm for underwater navigation

This work aims to develop and evaluate a navigation strategy based on optical payloads for Autonomous Underwater Vehicles (AUVs). The use of cameras for navigation purposes can make possible a correct vehicle localization in particular working conditions, where other sensors, such as Doppler Velocity Log (DVL), could not be used. In particular, feature detection and outliers removal algorithms have been chosen as possible critical step in the whole algorithm and have been carefully investigated. Underwater environment introduces challenging conditions for a feature based navigation system and, consequently, the images need to be firstly processed. The developed visual-inertial odometry (VIO) algorithm has been employed for the vehicle translation estimation and this information has been fused with the altimeter, Inertial Measurement Unit (IMU) and Fiber Optic Gyroscope (FOG) measurements. The developed algorithm was tested with an image set acquired by Zeno AUV in the Haifa Bay, Israel (September 2018) and with an image set acquired by FeelHippo AUV in Vulcano, Italy (June 2019) and the results were compared with the path estimated exploiting the other on-board sensors (e.g., the DVL, which has been considered as reference sensor for the benchmark path computation). The algorithm performances are evaluated in both cases, focusing either on the estimate quality and on the requested computational load.