Uncertainty-based Sensor Fusion of Range Data for Real-time Digital Elevation Mapping (RTDEM)

This paper introduces a new computationally inexpensive approach to perception and modeling of the environment that allows fusion of sensory range data of various types and fidelities while explicitly taking into account a complete description of uncertainty of the range measurements. This approach makes use of known sensor uncertainty models to create a single 2.5D digital elevation map whose accuracy is robust to sensor noise and spurious data. This approach is particularly suitable for real-time application in high speed and highly unstructured outdoor environments for which reasonably accurate and timely vehicle state estimates are available. Experimental results are presented in which LADAR range measurements and state estimates are combined according to this approach. We provide qualitative comparison to other classes of environment modeling.