Accurate calibration can develop the role of a focused plenoptic camera in the fields like scene reconstruction and non-contact measurement. One of the keys to improve the calibration precision is the accurate feature extraction algorithm. In order to improve the accuracy and efficiency of feature detection, we present a checkerboard corner detection algorithm based on the raw images. First, a robust corner detection operator is used to detect the checkerboard corners in the raw images, and the corresponding relationship between the 2D corners and the 3D plenoptic disc features is used to screen the detected results. Then, the sub-pixel optimization is carried out using the image consistency. The simulated corner detection and calibration experiments are carried out, and the distance measurement experiment is also carried out based on the reconstructed corners obtained by the R29 focused plenoptic camera. The experimental results show that the accuracy of the proposed corner detection algorithm is higher than those of the existing algorithms, and the calibration algorithm based on the proposed corner detection algorithm can achieve more accurate results.