An open-source software framework is presented that allows real-time underwater mapping with popular marine robotics components, namely a BlueRobotics BlueROV2 with its standard Ping360 Mechanical Scanning Sonar (MSS) and a A50 Doppler Velocity Log (DVL), which are low-cost devices for their respective types - if not even the most affordable ones on the market. The software runs with low computational power on a Raspberry Pi4. The framework builds upon Synthetic Scan Formation (SSF) where single MSS beams or scan-lines are embedded into a pose-graph. The rendering of scans is not only based on navigation, but based on the graph itself. Scans formed from scan-lines can be optimized by online Simultaneous Localization and Mapping (SLAM) and result in improved scans, based on the current state of the graph. In subsequent steps this leads to improved registration results. To this end, a combination of two different types of loop-closures is presented. Namely a consecutive loop closure, and a proximity based loop closure, which reduces the overall drift. The framework is validated in three different test-environments, namely a pool, a test-tank with a gantry for ground truth motion, and the flooded basement of a WW-II submarine bunker. Among others, it is shown that there is an increased accuracy compared to conventional SLAM and that the software is usable in real-time during a mission with the low-cost hardware.

In this paper, we introduce Fourier-SOFT 2D (FS2D) as a new robust registration method. FS2D operates in the frequency domain where it exploits the well-known decoupling of rotation and translation. The challenging part of determining the rotation parameter is solved here based on a projection of the Fourier magnitude on a sphere and the SO(3) Fourier Transform (SOFT). The underlying use case is underwater mapping with sonar, i.e., with very noisy and partially overlapping environment data under non-trivial localization and navigation challenges. Fourier-SOFT 2D is compared with openly available registration methods on two real-world datasets and a simulated dataset. Results show the robustness of FS2D, i.e., its capabilities to handle large amounts of noise and occlusions of consecutive scans. The implementation in C++ is openly available.

Despite the recent progress, guidance, navigation, and control (GNC) are largely unsolved for agile micro autonomous underwater vehicles (μAUVs). Hereby, robust and accurate self-localization systems which fit μAUVs play a key role and their absence constitutes a severe bottleneck in micro underwater robotics research. In this work we present, first, a small-size low-cost high performance vision-based self-localization module which solves this bottleneck even for the requirements of highly agile robot platforms. Second, we present its integration into a powerful GNC-framework which allows the deployment of μAUVs in fully autonomous mission. Finally, we critically evaluate the performance of the localization system and the GNC-framework in two experimental scenarios.