Probabilistic map fusion for fast, incremental occupancy mapping with 3D Hilbert maps

Abstract

We present a novel formulation of Hilbert mapping in which we construct a global occupancy map by incrementally fusing local overlapping Hilbert maps. Rather than maintain a single supervised learning model for the entire map, a new model is trained with each of a robot's range scans, and queried at all points within the robot's perceptual field. We treat the probabilistic output of the classifier as a sensor, employing sensor fusion to merge local maps. This formulation allows Hilbert mapping to be used incrementally in real-world mapping scenarios with overlap between sensor observations. The methodology is applied to three-dimensional map-building, and evaluated using real and simulated 3D range data.