Advanced vision systems for exploration by a planetary aerobot

Aerial platforms can become an integral part of surface exploration missions on planets or moons with an atmosphere, like Venus, Mars or Titan. One of the most immediate applications for aerobots is ultra-high resolution imaging over extensive areas of the planet. Planetary Aerobot missions could prove very useful in the automatic detection of geological features and selection of possible landing sites for a planetary exploration mission. The Aerobot system can travel across many areas on the planet and send the appropriate data back to Earth. Unfortunately the bandwidth available for data transmission from the planet to Earth is very limited. These bandwidth restrictions would require, if the Aerobot System were to transmit all collected imagery of the planet, heavy compression on the images. This compression would definitely hamper the scientists on Earth to determine the interesting areas. The computer vision algorithms used to reconstruct the terrain in 3D would be disadvantaged as well. It is therefore imperative that the Aerobot System has some degree of autonomy and can perform computer vision operations on its own which makes it possible to detect interesting geological features on the spacecraft. During the execution of its mission, the Aerobot System has access to the uncompressed imagery, taken by its camera(s). It is therefore recommended to perform all critical computer vision processing on the system itself before the images are polluted by compression. The generation of Digital Elevation Maps is clearly a computer vision process that suffers from compression. Generation of these maps on the Aerobot and sending them, together with the compressed reconstructed parts of the images, will lead to a much better understanding of the observed areas, for the same amount of expended bandwidth. In this paper, an Imaging and Localization Package (ILP) is described which is capable of performing the computer vision processing described above. All data collected by the Aerobot needs to be correlated with the position in which the measurement was acquired. On long duration missions, the Aerobot can not rely on localization performed by an orbiter or from ground; it must have its own means. During the last decade the computer vision community has made tremendous progress in acquiring 3D information from images taken by uncalibrated cameras, while at the same time self-calibrating the camera. The ILP makes use of these algorithms to compute both the calibration of the camera and the 3D reconstruction of the terrain. The specifics of an Aerobot mission, like almost linear motion of the camera, almost planar terrains, etc, require changes and new techniques in the reconstruction pipeline. Once calibration and reconstruction have been computed, scoring techniques can be applied to the data which now comprises of not only the images but of 3D information as well. The scoring algorithms should be written such that high scores for an image correspond to a large chance on interesting geological features to be found in that specific image. These scores can