Range data merging for probabilistic octree modeling of 3D workspaces

In a previous paper by Payeur et al. (1997), probabilistic occupancy modeling has been successfully extended to 3D environments by means of a closed-form approximation of the probability distribution. In this paper, the closed-form approximation is revisited in order to provide more reliable and meaningful models. A merging strategy of local probabilistic occupancy grids originating from each sensor viewpoint is introduced. The merging process takes advantage of the multiresolution characteristics of octrees to minimize the computational complexity and enhance performances. An experimental testbed is used to validate the approach and models computed from real range images are presented.