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Global registration of heterogeneous ground and aerial mapping data is achallenging task. This is especially difficult in disaster response scenarioswhen we have no prior information on the environment and cannot assume theregular order of man-made environments or meaningful semantic cues. In thiswork we extensively evaluate different approaches to globally register UGVgenerated 3D point-cloud data from LiDAR sensors with UAV generated point-cloudmaps from vision sensors. The approaches are realizations of differentselections for: a) local features: key-points or segments; b) descriptors:FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.Additionally, we compare the results against standard approaches like applyingICP after a good prior transformation has been given. The evaluation criteriainclude the distance which a UGV needs to travel to successfully localize, theregistration error, and the computational cost. In this context, we report ourfindings on effectively performing the task on two new Search and Rescuedatasets. Our results have the potential to help the community take informeddecisions when registering point-cloud maps from ground robots to those fromaerial robots.

3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation