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In this paper, we propose a novel approach for underwater simultaneous localization and mapping using a multibeam imaging sonar for 3D terrain mapping tasks. The high levels of noise and the absence of elevation angle information in sonar images present major challenges for data association and accurate 3D mapping. Instead of repeatedly projecting extracted features into Euclidean space, we apply optical flow within bearing-range images for tracking extracted features. To deal with degenerate cases, such as when tracking is interrupted by noise, we model the subsea terrain as a Gaussian Process random field on a Chow–Liu tree. Terrain factors are incorporated into the factor graph, aimed at smoothing the terrain elevation estimate. We demonstrate the performance of our proposed algorithm in a simulated environment, which shows that terrain factors effectively reduce estimation error. We also show ROV experiments performed in a variable-elevation tank environment, where we are able to construct a descriptive and smooth height estimate of the tank bottom.