Autonomous underwater vehicles (AUVs) can operate without the need for human control or tether cables as long as there is sufficient energy. AUVs have recently been used for seafloor imaging. Visual observation by AUVs provides high-resolution color information of the seafloor. However, conventional observation techniques that follow a prespecified path offer limited coverage because it is impossible for operators to build a suitable path in unknown rough terrain. A flawed prespecified path will produce incomplete observation. If unobserved areas are found during postprocessing, another dive is necessary, which increases the total cost. To overcome this problem, the authors have proposed a path replanning method to realize high-coverage observation in one dive. With this method, the AUV evaluates unobserved areas after the first prespecified observation; if unobserved areas are found, the AUV recreates an appropriate path to cover what was missed. The validity of the proposed method was previously evaluated using an artificial target in a tank and in shallow seas at a depth of approximately 35 m. In this study, the feasibility of the method was validated in a more challenging setting: experimental data were taken from a hydrothermal vent field in Kagoshima Bay, Japan.