An appraisal of backscatter removal and refraction calibration models for improving the performance of vision-based mapping and navigation in shallow underwater environments

Vision-based mapping (VbM) is one of the fundamental origins of automation in remote and autonomous spatial data acquisitions. Complexity in obtaining accurate data arises when such method is applied in the underwater environment. This paper proposes an integrated backscatter removal and refraction calibration model within the VbM and navigation pipeline. It is argued that the proposed VbM-dedicated models can significantly improve the conformity of objects’ positions underwater around the camera's motion. The study uses automatic refraction adjustment with a correction map to undistort images in real-time, addressing issues including refraction distortion and non-uniform light exposure that arise in underwater environments. Furthermore, haze removal, contrast adjustment, and color correction are combined to achieve backscatter removal. The results show that these actions help lessen the difficulties of submerged environments. The study employs simulation datasets to evaluate the sensitivity to varying turbidity levels. Additionally, field experiments with GoPro 10 hardware in Pramuka Island Waters, Indonesia, offer real-world context for the study's relevance to distinct underwater circumstances. The research shows efficient backscatter removal improves feature detection robustness, especially in murky water conditions. Refraction correction also successfully eliminates the bowing effect from missing ground control points in underwater environments. The study is significant because it emphasizes how vital image enhancement and refraction calibration are to obtaining centimeter-level map accuracy of VbM. The results highlight the need for a comprehensive strategy to advance underwater mapping and navigation technology to deliver accurate and dependable outcomes in various underwater situations.