We present a new collision detection algorithm to perform contact computations

between noisy point cloud data. Our approach takes into account the uncertainty that

arises due to discretization error and noise, and formulates collision checking as a two-class

classification problem. We use techniques from machine learning to compute the collision

probability for each point in the input data and accelerate the computation using stochastic

traversal of bounding volume hierarchies. We highlight the performance of our algorithm

on point clouds captured using PR2 sensors as well as synthetic data sets, and show that

our approach can provide a fast and robust solution for handling uncertainty in contact

computations.