Underwater SLAM using sonar imagesSamec, Jaša (Avtor) Skočaj, Danijel (Mentor) Dobrevski, Matej (Komentor) Underwater roboticsImaging sonarOdometryPoint trackingMotion estimationLoop closureSLAMUnderwater vehicles have no access to GNSS, so a vehicle below the surface must estimate its trajectory by composing relative motion increments. Optical cameras fail in turbid water, which leaves the forward-looking imaging sonar as the most practical sensor for perception at long range. Sonar imagery is noisy, artifact-heavy, and weakly textured, so the pairwise keypoint matching on which existing odometry methods rely is fragile. We present ISOPoT, an imaging sonar odometry method that builds correspondences from multi-frame point tracks produced by a modern tracking-any-point model and converts them into a single globally consistent planar motion estimate through grid-based point management and correlation-based refinement. On the public Aracati 2017 dataset and on Portorož 2025, a dataset collected for this work, ISOPoT attains lower translation and rotation error than prior sonar odometry baselines in both the sonar-only and auxiliary-sensor (dead reckoning and magnetometer) settings. Finally, we close loops from sonar alone. An appearance-based detector proposes revisits and a learned relative-pose model turns them into pose-graph constraints, and neither stage consults the trajectory estimate, so detection does not fail precisely when the drift has grown large enough to require correction. On both Portorož 2025 sequences the accepted closure reduces absolute trajectory error with no auxiliary sensor.20262026-09-16 10:40:01Magistrsko delo/naloga187932VisID: 38475sl