We present a method of indexing 3D objects from single 2D images. The method does not rely on invariant features. This allows a richer set of shape information to be used in the recognition process. We also suggest the kd-tree as an alternative indexing data structure to the standard hash table. This makes hypothesis recovery more efficient in high-dimensional spaces, which are necessary to achieve specificity in large model databases. Search efficiency is maintained in these regimes by the use of best-bin first search. Neighbors recovered from the index are used to generate probability estimates, local within the feature space, which are then used to rank hypotheses for verification. On average, the ranking process greatly reduces the number of verifications required. Our approach is general in that it can be applied to any real-valued feature vector. In addition, it is straightforward to add to our index information from real images regarding the true probability distributions of the feature groupings used for indexing.

We present an integrated system in which an operator uses a simulated environment to program part-mating and contact tasks. Generation of models within this virtual environment is facilitated using a fast, occlusion tolerant, 3D grey-scale vision system which can recognize and accurately locate objects within the work site. A major goal of this work is to make robotic programming easy and intuitive for untrained users working with standard desktop hardware. Simulation offers the ease-of-use benefits of "programming by demonstration", coupled with the ability to create a programmer-friendly virtual environment. Within a simulated environment, it is also straightforward to track and interpret an operator's actions. The simulator models objects as polyhedra and implements full 3D contact dynamics. When a manipulation task is completed, local planning techniques are used to turn the virtual environment's motion sequence history into a set of robot motion commands capable of realizing the prescribed task.

Shape indexing is a way of making rapid associations between features detected in an image and object models that could have produced them. When model databases are large, the use of high-dimensional features is critical, due to the improved level of discrimination they can provide. Unfortunately, finding the nearest neighbour to a query point rapidly becomes inefficient as the dimensionality of the feature space increases. Past indexing methods have used hash tables for hypothesis recovery, but only in low-dimensional situations. In this paper we show that a new variant of the k-d tree search algorithm makes indexing in higher-dimensional spaces practical. This Best Bin First, or BBF search is an approximate algorithm which finds the nearest neighbour for a large fraction of the queries, and a very close neighbour in the remaining cases. The technique has been integrated into a fully developed recognition system, which is able to detect complex objects in real, cluttered scenes in just a few seconds.

We describe an implemented model-based telerobotic system designed to investigate assembly and other tasks involving contact and manipulation of known objects. Key features of our system include ease of maintaining a world model at the operator site and a task-centric operator interface. Our system incorporates gray-scale model-based vision to assist in building and maintaining the local model. The local model is used to provide a task-centric operator interface, emphasizing the natural and direct manipulation of objects, with the robot's presence indicated in a more abstract fashion. The operator interface is designed to work with widely available and inexpensive desktop computers with low DOF input devices (such as a mouse). We also describe experimental results to date, which include performing assembly-like tasks over the Internet.

Indexing is an efficient method of recovering match hypotheses in model-based object recognition. Unlike other methods, which search for viewpoint-invariant shape descriptors to use as indices, we use a learning method to model the smooth variation in appearance of local feature sets (LFS). Indexing from LFS effectively deals with the problems of occlusion and missing features. The indexing functions generated by the learning method are probability distributions describing the possible interpretations of each index value. During recognition, this information can be used to select the least ambiguous features for matching. A verification stage follows so that the final reliability and accuracy of the match is greater than that from indexing alone. This approach has the potential to work with a wide range of image features and model types.>