Non-parametric occupancy map using millions of range data

This paper presents a fast method to estimate the probability of occupancy of a space point from a huge set of 3D rays represented in a common reference. These data can come from any range finding sensor such as : Lidar, Kinect or Velodyne. The key idea is to consider that the occupancy of a space 3D point is linked to 1) the number of 3D point belonging to a local volume around the point and 2) the number of rays crossing through the same volume. We propose a probabilistic non-parametric framework based on KNN estimator. The major contribution of the paper is an original solution to search rays in the neighborhood of a 3D point with a five dimensional binary tree that can handle several millions measurements. Experiments shows the relevance of the proposed method in terms of both accuracy and computation time. Moreover, the resulting method has been applied to three different 3D sensors: a Kinect, a 3D Lidar (Velodyne HDL-64E) and a mono-planar Lidar.