Iterative Closest Point with Minimal Free Space Constraints

Abstract

The Iterative Closest Point (ICP) method is widely used for fitting geometric models to sensor data. By formulating the problem as a minimization of distances evaluated at observed surface points, the method is computationally efficient and applicable to a rich variety of model representations. However, when the scene surface is only partially visible, the model can be ill-constrained by surface observations alone. Existing methods that penalize free space violations may resolve this issue, but require that the explicit model surface is available or can be computed quickly, to remain efficient. We introduce an extension of ICP that integrates free space constraints, while the number of distance computations remains linear in the scene’s surface area. We support arbitrary shape spaces, requiring only that the distance to the model surface can be computed at a given point. We describe an implementation for range images and validate our method on implicit model fitting problems that benefit from the use of free space constraints.