Accurate and reliable reconstruction of three-dimensional underwater environments remains a persistent challenge, particularly when employing low-cost acoustic sensors onboard compact robotic platforms. In this work, an integrated multi-modal data-fusion framework is presented, which combines a mechanical scanning sonar, an inertial measurement unit (IMU), and a depth sensor to generate high-fidelity 3D boundary representations of underwater scenes. The proposed framework incorporates motion-compensation and depth-anchoring mechanisms that mitigate the effects of slow update rates and motion distortions inherent to mechanical scanning sonars; hence, two-dimensional acoustic profiles are consistently extended into boundary-layer point clouds. The controlled experiments were conducted in a saltwater pool environment by using ground-truth anchors for quantitative validation. The fusion system achieved a root-mean-square error of 6.2 cm, a mean absolute deviation of 4.8 cm, and a standard deviation of 2.1 cm, thereby demonstrating reliable sub-decimeter accuracy. Compared with sonar-only reconstructions, which yielded a root-mean-square error of 9.4 cm, the proposed fusion approach improved accuracy by approximately 35 percent. Vertical consistency was also maintained, with average deviations across depth layers of 5.5 cm. The reconstructed maps accurately represent both linear and curved geometries, confirming the robustness and accuracy of the proposed framework. Experimental results demonstrate that this low-cost multi-sensor fusion approach is a useful and efficient method for various marine applications, such as aquaculture monitoring, underwater infrastructure inspection, and confined space surveys. • Integrated Multi-Sensor Fusion Framework: Proposes a novel framework combining a low-cost mechanical scanning sonar (Ping360), an IMU, and a depth sensor for high-fidelity 3D underwater mapping. • Motion and Distortion Compensation: Implements an advanced "Planar Fusion" pipeline with motion-compensation and depth-anchoring to mitigate the inherent slow update rates and motion distortions of mechanical sonar. • Sub-Decimeter Mapping Accuracy: Achieved a root-mean-square error (RMSE) of 6.2 cm, representing a 35% accuracy improvement compared to traditional sonar-only reconstruction methods. • Robust Vertical Consistency: Demonstrates stable 3D volumetric reconstruction with an average deviation of only 5.5 cm across depth layers, effectively preventing cumulative Z-axis drift. • Cost-Effective Robotic Solution: Offers a scalable and efficient mapping approach suitable for compact robotic platforms operating in confined spaces, aquaculture, and infrastructure inspection.
Sukmanee et al. (Fri,) studied this question.