Visual degradation in underwater environments poses unique and significant challenges, which distinguishes underwater SLAM from popular vision-based SLAM on the ground. In this paper, we propose RUSSO, a robust underwater SLAM system which fuses stereo camera, inertial measurement unit (IMU), and imaging sonar to achieve robust and accurate localization in challenging underwater environments for 6 degrees of freedom (DoF) estimation. During visual degradation, the system is reduced to a sonar-inertial system estimating 3-DoF poses. The sonar pose estimation serves as a strong prior for IMU propagation, thereby enhancing the reliability of pose estimation with IMU propagation. Additionally, we propose a SLAM initialization method that leverages the imaging sonar to counteract the lack of visual features during the initialization stage of SLAM. We extensively validate RUSSO through experiments in simulator, pool, and sea scenarios. The results demonstrate that RUSSO achieves better robustness and localization accuracy compared to the state-of-the-art visual-inertial SLAM systems, especially in visually challenging scenarios. To the best of our knowledge, this is the first time fusing stereo camera, IMU, and imaging sonar to realize robust underwater SLAM against visual degradation.

The application of Large Language Models (LLMs) in analog integrated circuit design could pioneer a new era of AI applications in domains traditionally dominated by human expertise. Analog semiconductor chips are the core building blocks in sensing and communication systems. Contrary to digital chip development, where computer-aided design automation has been widely adopted for a few decades, analog design, often perceived more as a craftsmanship than a well-established engineering procedure, relies heavily on the designer's experience and intuition to navigate in the trade space of efficiency, noise, linearity, and speed to meet certain specifications. This domain's depth, requiring a blend of acumen and creativity, underscores the high barriers to entry and the extensive training required to master its intricacies, which exacerbated the critical labor shortfall of the semiconductor industry in this decade [Ravi, 2023]. The advent of AI-assisted design automation in analog circuit design holds considerable promise to tackle the aforementioned challenge. It offers the potential to significantly streamline design cycles, enabling engineers to focus more on strategic, high-level design considerations and the exploration of novel ideas and applications.