The work proposed in this document aims to investigate the use of consumer RGB-D sensors for object detection and pose estimation from natural features, with the purpose of using such techniques for developing augmented reality applications. Two methods based on depth-assisted rectification are proposed, which transform features extracted from the color image to a canonical view using depth data in order to obtain a representation invariant to rotation, scale and perspective distortions. While one method is suitable for textured objects, either planar or non-planar, the other method focuses on texture-less planar objects. Qualitative and quantitative evaluations of the proposed methods are performed, comparing it to existing methods for object detection and pose estimation.