Good features to track for RGBD images

RGBD (texture-plus-depth) image representation enriches traditional 2D content with additional geometrical information, having the potential to improve the performance of many computer vision tasks. In image matching, this has been partially studied by considering how depth maps can help render feature descriptors more distinctive. However, little has been done to design keypoint detection approaches able to leverage the availability of depth information. In this paper, we propose a novel and robust approach for detecting corners from RGBD images. Our method modifies a classical corner detection strategy, based on local second-order moment matrices, by computing derivatives in a coordinate system which reflects the local properties of object surfaces. Our results demonstrate a higher stability to out-of-plane rotations of the proposed RGBD corner detector both in terms of feature repeatability and in a visual odometry application.