Acoustic positioning systems or inertial measurement units (IMUs) could be used in indoor mobile robot localization. Acoustic systems, especially those based on SSSound, offer centimeter-level absolute positioning but suffer from motion-induced Doppler effects and time delays due to time-division scheduling. IMUs provide self-contained, high-rate motion data but are limited by cumulative drift over time. Hybrid systems that combine these modalities can mitigate each other’s weaknesses, achieving both continuity and long-term accuracy. This chapter presents the foundational methods and architectures for such sensor fusion, with a focus on Kalman filtering and posterior probabilistic approaches. It also extends the discussion to multimodal systems that incorporate vision, LiDAR, and UWB ranging, highlighting their integration challenges and performance benefits in real-world environments such as greenhouses and factories.