Tightly Coupled, Graph-Based DVL/IMU Fusion and Decoupled Mapping for SLAM-Centric Maritime Infrastructure Inspection

In this article, we address the problem of simultaneous localization and mapping (SLAM)-centric maritime infrastructure inspection [using unmanned surface vehicles (USVs)] via novel approaches in tightly-coupled, graph-based DVL/IMU fusion and decoupled mapping. As our first contribution, we formalize the preintegration of linear velocity measurements, obtained by a Doppler velocity log (DVL), in combination with angular velocity measurements, obtained by an inertial measurement unit (IMU), as binary factors encoding relative position. To evaluate state estimation improvements imparted by DVL/IMU fusion, we implement our proposed factor within a state-of-the-art, graph-based lidar-visual-inertial (LVI) SLAM system as our second contribution. Accuracy and robustness improvements are demonstrated in simulation by comparing maximum
a posteriori
pose estimates with and without DVL/IMU fusion against ground truth poses. As our third contribution, we propose a map generation framework for downstream inspection applications decoupled from SLAM. In our framework, volumetric data (captured by sonar, lidar, etc.) is transformed into a common world coordinate frame using extrinsic calibrations and SLAM pose estimates as input. Our framework operates over the complete set of raw volumetric data, whereas SLAM systems (both online and offline) typically operate over a subset of down-sampled volumetric data. To address the processing of additional volumetric data, we present innovations in refined pose correction and staged filtering for user-controlled denoising. We experimentally evaluate our map generation framework against the LVI SLAM system adopted for this study using real-world data and demonstrate improvements to map quality metrics important to inspection.