Joint underwater mapping with acoustic and optical Iimages

Abstract

This thesis is developed within the context of the IURBI project [1], which seeks to develop an intelligent AUV capable of real-time seafloor analysis and adaptive mission planning (Figure 1.1). A fundamental prerequisite for such autonomous capabilities is the ability to robustly align and fuse sensor data from multiple sources and surveys into a single, coherent model. This thesis addresses that foundational challenge by developing a comprehensive offline framework for multi-session, multimodal map alignment. The primary objectives of this thesis are to: – Develop a robust and flexible framework for the alignment and integration of side-scan sonar and optical imagery acquired in single or multiple sessions by AUVs, towfish, or ROVs. – Formulate and implement a factor graph optimization approach to jointly re fine vehicle trajectories and sensor alignments across multiple sessions and modalities, accommodating the inherent uncertainties in underwater navigation. – Evaluate the performance of the proposed methodology using real-world under water datasets, assessing its accuracy, robustness, and practical applicability. The scope of this work encompasses the offline processing and alignment of pre viously collected side-scan sonar and optical image datasets. While initial navigation data from the AUV/ROV is assumed to be available, this work specifically focuses on refining these initial pose estimates to achieve precise multimodal and multi-session co-registration.

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