| Abstract | The problem of pose estimation arises in many areas of computer vision, including object recognition,object tracking, site inspection and updating, and autonomous navigation when scene models are avail-
able. We present a new algorithm, called SoftPOSIT, for determining the pose of a 3D object from a
single 2D image when correspondences between model points and image points are not known. The
algorithm combines Gold’s iterative softassign algorithm [20, 21] for computing correspondences and
DeMenthon’s iterative POSIT algorithm [14] for computing object pose under a full-perspective camera
model. Our algorithm, unlike most previous algorithms for pose determination, does not have to hypoth-
esize small sets of matches and then verify the remaining image points. Instead, all possible matches
are treated identically throughout the search for an optimal pose. The performance of the algorithm is
extensively evaluated in Monte Carlo simulations on synthetic data under a variety of levels of clutter,
occlusion, and image noise. These tests show that the algorithm performs well in a variety of difficult sce-
narios, and empirical evidence suggests that the algorithm has an asymptotic run-time complexity that is
better than previous methods by a factor of the number of image points. The algorithm is being applied
to a number of practical autonomous vehicle navigation problems including the registration of 3D archi-
tectural models of a city to images, and the docking of small robots onto larger robots.
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