USBL systems are essential for providing accurate positions of autonomous underwater vehicles (AUVs). On the other hand, the accuracy can be degraded by outliers because of the environmental conditions. A failure to address these outliers can significantly impact the reliability of underwater localization and navigation systems. This paper proposes a novel outlier rejection algorithm for AUV localization using Voronoi diagrams and query point calculation. The Voronoi diagram divides data space into Voronoi cells that center on ultra-short baseline (USBL) data, and the calculated query point determines if the corresponding USBL data is an inlier. This study conducted experiments acquiring GPS and USBL data simultaneously and optimized the algorithm empirically based on the acquired data. In addition, the proposed method was applied to a sensor fusion algorithm to verify its effectiveness, resulting in improved pose estimations. The proposed method can be applied to various sensor fusion algorithms as a preprocess and could be used for outlier rejection for other 2D-based location sensors.

Recent advances in forward-looking sonar (FLS) have enabled the acquisition of high-resolution acoustic images. However, the accuracy of image-based rotation estimation remains limited owing to speckle noise, perceptual ambiguity, and shadows. In recent years, object-based path reconstruction has become increasingly important for underwater inspection tasks, and in such scenarios, reliably estimating rotation from static seabed objects is essential for ensuring the robustness of autonomous underwater vehicle (AUV) missions. Accordingly, we present a rotation estimation method that adaptively extracts a region of interest (ROI) and applies the Radon transform. The proposed approach automatically selects sonar image regions containing objects and emphasizes high projection values in the resulting sinogram. By computing the shift between the high projection values of two sinograms, the method achieves robust rotation estimation even under low contrast and severe speckle noise. Experimental results demonstrate that our method consistently achieves lower estimation errors than existing approaches, particularly in scenarios involving static seabed objects. These findings highlight its practical value for object-based path reconstruction, high-precision mapping, and other underwater navigation tasks.