Aiming at challenges encountered during traditional handheld scanning， such as difficulty in aligning the robotic-assisted optical coherence tomography （OCT） probe with the target， inaccurate probe posture， and hand tremors of the operator， this article presents a study on high-precision localization and flexible posture adjustment of the OCT probe to meet dynamic imaging requirements in surgical procedures. To improve the localization accuracy and achieve the posture adjustment， we propose a method for system calibration and OCT probe pose optimization， ensuring that the probe is positioned in the optimal imaging posture. Firstly， the system calibration is implemented with the corresponding pixel domain to spatial domain conversion coefficients and Tsai-Lenz method. The pose optimization of the OCT probe can be optimized with image processing. The Laplacian random walk algorithm is used to obtain the skin phantom surface contour， thus calculating the normal vector of the skin phantom surface. Meanwhile， the contour of vessel phantom is segmented with the convolutional neural network method. The radius vector of vessel phantom is obtained with the segmented results. There are two vectors that determine the attitude of the probe， in which Vp and Ve denote the unit vectors parallel to the optical axis and the B-scan direction， respectively. When the OCT probe is in the optimal imaging pose， Vp should be parallel to p and Ve should be parallel to b. Using a robotic-assisted OCT imaging system to image vascular and skin phantoms， 20 repeated experiments are conducted. The positioning error of the probe in the X-Y plane is ±0.21 mm， and the positioning error in the Z direction is ±0.33 mm. The system offers advantages such as multi-degree-of-freedom， high imaging accuracy， and good stability， and can assist doctors in quantitatively assessing the condition of blood vessels.