The parameter error of inertial navigation system has important effect on the precision of navigation system. Conventional method of angular velocity calibration can’t identify the bias and the error calibration of the Fiber Optic Gyroscope (FOG) under the insufficient preheat condition is affected by temperature fluctuation. To solve the two problems, a new calibration algorithm for FOG Strapdown Inertial Navigation System (SINS) was brought forward, called Adaptive Recursive Least Square (ARLS). After analyzing the FOG Inertial Measurement Unit (IMU) errors and their compensation model, the method of adaptive forgetting factor was studied through large temperature tests and implementation procedure was given in detail. Finally, the validity of ARLS that can identify the error coefficient of FOG, and restrain the effect of temperature fluctuation in the condition of unstable device characteristics, is proved by algorithm emulation and angular velocity test.