The success of NASA's Mars Exploration Rovers has demonstrated the important benefits that mobility adds to planetary exploration. Very soon, mission requirements will impose that planetary exploration rovers drive autonomously in unknown terrain. This will require an evolution of the methods and technologies currently used. This paper presents our approach to 3D terrain reconstruction from large sparse range data sets, and the data reduction achieved through decimation. The outdoor experimental results demonstrate the effectiveness of the reconstructed terrain model for different types of terrain. We also present a first attempt to classify the terrain based on the scans properties.
In this paper we present our approach to 3D surface reconstruction from large sparse range data sets. In space robotics constructing an accurate model of the environment is very important for a variety of reasons. In particular, the constructed model can be used for: safe tele-operation, path planning, planetary exploration and mapping of points of interest. Our approach is based on acquiring range scans from different view-points with overlapping regions, merge them together into a single data set, and fit a triangular mesh on the merged data points. We demonstrate the effectiveness of our approach in a path planning scenario and also by creating the accessibility map for a portion of the Mars Yard located in the Canadian Space Agency.
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