In this paper we present a new complete detector–descriptor framework for local features extraction from grayscale texture-plus-depth images. It is designed by putting together a locally normalized binary descriptor and the popular AGAST corner detector modified to incorporate the depth map into the keypoint detection process. With these new local features, we target image matching applications when significant out-of-plane rotations and viewpoint position changes are present in the input data. Our approach is designed to perform on RGBD images acquired with low-cost sensors such as Kinect without any complex depth map preprocessing such as denoising or inpainting. We show improved results with respect to several other highly competitive local image features through both a classic local feature evaluation procedure and an illustrative application scenario. Moreover, the proposed method requires low computational effort.