N 93-1 ! 955 Model-Based Vision for Space Applications

This paper describes a method for tracking moving image features by combining spatial and temporal edge information with model based feature information. The algorithm updates the two-dimensional position of object features by correlating predicted model features with current image data. The results of the correlation process are used to compute an updated model. The algorithm makes use of a high temporal sampling rate with respect to spatial changes of the image features and operates in a real-time multi-processing environment. Preliminary results demonstrate successful tracking for image feature velocities between 1.1 and 4.5 pixels every image frame. This work has applications for docking, assembly, retrieval of floating objects and a host of other space-related tasks. 1, Introduction The ability to visually track an object during arbitrary motion is an important part of interacting with the environment. Humans adeptly recover three-dimensional structure of an object from its rigid-body motion in order to accomplish manipulation, locomotion, and object recognition [6]. Motion and edge information are known to be important cues to the recovery of object structure. Understanding the relative motion between an object and an observer aids not only in the recovery of object structure but also provides useful information required to interact with the objects. In order to visually track an object, the object's orientation and position must be rapidly updated. In six degree-of-freedom robotic applications, realtime camera images can provide a dense stream of data from which to extract object features and recover rigid body motion. An object's three-dimensional structure can be reconstructed from the projection of its motion onto a sequence of two-dimensional images [22]. Two methods for measuring visual motion are detection of spatio-temporal intensity changes and feature tracking [18]. The measurement of spatio-temporal intensity changes can be accomplished using correlation models, energy filters [1] [4], or gradient techniques [21 [13]. These algorithms provide a description of two-dimensional image motion as a result of changes in intensity in the image. They cannot differentiate between intensity differences due to changes in viewer motion and intensity changes produced by object motion with respect to a light source. Thus, they are not suited to the task of tracking a moving object. The class of algorithms that measure visual motion by tracking features provides a means to directly measure physical motion in the world. These algorithms, however, have the disadvantage of requiring feature correspondence between images, By combining the feature tracking approach with modeldriven techniques, the feature tracking process is constrained and the correlation of features between frames is simplified. This results in a computationally inexpensive and accurate system. This paper describes an approach designed to achieve these goals and the implementation of this approach in the Intelligent Controls Group (ICG) laboratory at the National Institute of Standards and Technology. The next section discusses model-based feature tracking methods and, in particular, the two-dimensional tracking method we use. Section 3 details the implementation of the algorithm in our lab. The fourth section quantifies the accuracy and speed of this algorithm in preliminary experiments by tracking a planar target, and the final section discusses the implications of these results. 2. Model-based Feature Tracking Model-based feature tracking correlates image features with object model features to take advantage of model information. Correlation between extracted features and an object model can be performed in either a two-dimensional [9] or three-dimensional [12] [19] frame-of-reference. A useful survey of work done using these methods can be found in [20], Three-dimensional tracking involves comparing a set of two dimensional features extracted from an image to a three-dimensional model. In the most general case, this means solving the three-dimensional recognition problem for each successive image frame. The three-dimensional position and orientation of the feature are computed by analyzing images taken at different positions. Correspondence of features between image frames is determined, and the three-dimensional feature position is computed and matched to the model. This process is time consuming computationally expensive. A more efficient way of using a three-dimensional frame of reference for model matching involves computing only the changes in structure position and orientation between image 220 https://ntrs.nasa. gov/search.jsp? R=19930002767 2018-09-23T15:57:39+00:00Z