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Cited by 624

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

57th International Astronautical Congress

·2006

Abstract

ESA is planning a series of robotic missions to Mars, the safe delivery of which will be greatly assisted by landing systems capable of autonomous navigation and hazard detection and avoidance. Vision-based navigation is a promising technique which is currently being developed by ESA. The testing of vision-based navigation systems can benefit from computer-based planet surface simulations representative of the target planetary body. PANGU (Planet and Asteroid Natural Scene Generation Utility) is a software tool for simulating and visualising the surface of various planetary bodies. PANGU builds a planet surface model starting with a predefined surface or a fractal surface generated by PANGU. Craters are placed on the terrain according to user defined parameters, combining idealised mathematical models with fractal techniques to produce a realistic appearance. Boulders, sand dunes and other small scale features may then be added. Given the position and orientation of a camera, PANGU generates the corresponding image accounting for illumination conditions. PANGU has been designed to give a high degree of realism while operating at near real-time speeds. As well as cameras, scanning LIDAR and radar altimeters may also be simulated, allowing a comprehensive evaluation of various navigation sensor combinations and redundancy schemes for planetary landers.

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Metadata

Published
2006-10-02
OpenAlex
W4212923100
Cited by
624
References
2
Type
paratext
Language
en
Open access
closed

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CoChat. (2026). Citation check: 57th International Astronautical Congress. https://app.cochat.ai/papers/10.2514/miaf06
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57th International Astronautical Congress. (2006). https://doi.org/10.2514/miaf06

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.