Transparent objects are ubiquitous in human environments but, due to their special interaction with light, very few vision methods exist to identify them. We propose a new algorithm for recognition and pose estimation of rigid transparent objects which can deal with overlapping instances and cluttered environments. Using an active depth sensor for segmentation of the objects and 2d edge analysis for pose estimation, we are able to provide accurate identification and position. The proposed method is evaluated on a Microsoft Kinect and also on a PR2 robot. Results show that the algorithm is robust and accurate enough for robotic grasping and that it can be used in practical applications like table cleaning.

Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.