This paper deals with automatically learning the spatial 
distribution of a set of images.  
That is, given a sequence of images acquired from well-separated locations, how 
can they be arranged to best determine where they were viewed from, relative to
one onother.
The solution to this problem can be viewed as an instance of 
robot mapping although it can also be used in other contexts.  
We examine the problem where only limited prior odometric information is 
available, employing a feature-based method derived from a 
probabilistic pose estimation framework.  Initially, a set of visual features are 
selected from the images and correspondences are found across the ensemble.  
The images are then localized by first assembling the small subset of images 
for which odometric confidence is high, and sequentially inserting the remaining images, 
localizing each against the previous estimates, and taking advantage of 
any  priors that are available. We present experimental results validating the approach, 
and demonstrating metrically and topologically accurate results over 
two large image ensembles.  Finally, we discuss the results, 
their relationship to the autonomous exploration of an 
unknown environment, and their utility for robot localization and navigation.