In this paper an evaluation of image keypoints detectors and descriptors is presented when used for building panoramic image. The descriptors: (SIFT, SURF, BRIEF, ORB, BRISK, and FREAK) were discussed, when used with the appropriate keypoints detectors on database taken indoors by RGB-D camera. Crosscheck and RANSAC (RANdom Sample Consensus) algorithms were used to find transform matrix between images. The speed of keypoints detectors and descriptors, the matching speed, the average of extracted keypoints, recall and precision were investigated. Oxford dataset was used to find the best descriptor for dealing with rotation and illumination changes that might occur due to changes in illumination angle. The obtained results showed that SIFT was the keypoint descriptor with the highest performance in non-real time applications. The SURF/BRISK was the best detector/descriptor which can be used in real time applications with comparable SIFT's results.