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Abstract: Finding objects and tracking their poses are essential functions for service robots, in order to manipulate objects and interact with humans. We present novel algorithms for local feature matching for object detection, and 3D pose estimation. Our feature matching algorithm takes advantage of local geometric consistency for better performance, and the new 3D pose estimation algorithm solves the pose in a closed-form using homography, followed by a non-linear optimization step for stability. Advantages of our approach include better performance, minimal prior knowledge for the target pattern, and easy implementation and portability as a modularized software component. We have implemented our approach along with both CPU and GPU-based feature extraction, and built an interoperable component that can be used in any Robot Technology (RT)-based control system. Experiment shows that our approach produces very robust results for the estimated 3D pose, and maintain very low false positive rate. It is also fast enough to be used in on-line applications. We integrated our vision component in an autonomous robot system with a search-and-grasp task, and tested it with several objects that are found in ordinary domestic environment. We present the details of our approach, the design of our modular component design, and the results of the experiments in this paper. [Copyright &y& Elsevier]