This paper proposes a sampling-based visual path planning framework for the servoing of a multirotor unmanned aerial vehicle. There are two main problems of the traditional image-based visual servoing: one is that it cannot guarantee the bounded control input because of the high disparity between the initial and desired features, the other is that it could lead to slow convergence rates caused by exponential attenuation over time and insufficient control inputs in the final phase. To overcome these problems, this paper proposes a sampling-based visual path planning framework to generate control inputs using reference features over the interval between the initial and desired features. Our proposed contribution can generate small and bounded control inputs in the large pose difference environments. In detail, it can maintain a small bounded error task function because of preventing singularities, uncertainties, and local minimums during calculating image Jacobian. Therefore, our proposed contribution can not only overcome the deficiencies of traditional image-based visual servoing approaches but also improve control performance. A variety of simulation results conducted to verify the performance of the proposed framework indicate that it can overcome the vulnerabilities of traditional image-based visual servoing with respect to large disparity environment and generate relative body velocity commands.