Key points are not available for this paper at this time.
This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of ob jects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arising from diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those from an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accu rately enough for some task and, if not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees offreedom and provides a practical means of esti mating the relationships ( position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.
Building similarity graph...
Analyzing shared references across papers
Loading...
Randall C. Smith
Peter Cheeseman
The International Journal of Robotics Research
Ames Research Center
SRI International
Menlo School
Building similarity graph...
Analyzing shared references across papers
Loading...
Smith et al. (Mon,) studied this question.
www.synapsesocial.com/papers/69df51946324afb55d59277f — DOI: https://doi.org/10.1177/027836498600500404
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: