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Additional resources for Aerial Vehicles (2009)
28 Aerial Vehicles Kuipers, B. J. (2000). The spatial semantic hierarchy. Artificial Intelligence, Vol. 119, pp. 191233. ISSN 0004-3702. Martin, C. & Thrun, S. (2002). Real-time acquisition of compact volumetric 3D maps with mobile robots. In IEEE International Conference on Robotics and Automation, pp. C. Moravec, H. P. & Elfes, A. (1985). High resolution maps from wide angle sonar. In IEEE International Conference on Robotics and Automation, pp. 116-121. ; Inaba, M. & Inoue, H. (2001). Plane segment finder: Algorithm, implementation and applications.
Thirdly, an ‘algorithm implementation model’ must be defined for each function to integrate. This model identifies the data type and the procedures used in each function. It takes account some factors, such as the binary format, in order to optimize the digital calculations. The internal data types are then converted to the bit true representations that will be used in the hardware implementation, and the model is re-simulated to verify its performance with quantized coefficient values and limited data bit widths, which can lead to overflow, saturation and scaling problems.
8ms. Accordingly, the sampling frequency fSmin will have to be set at values of at least 500Hz. When considering a MAV flying over an unknown terrain, we selected a minimum sampling frequency of 1kHz (Aubépart & Franceschini, 2007). The maximum sampling frequency fSMAX is less constraining to choose. , 1997). On the other hand, the maximum sampling frequency fSMAX, is limited by the lower end of the illuminance range over which the sensor is intended to operate, because at low illuminance levels, the integration of the photoreceptor signal takes a relatively long time and the sampling procedure will have to wait for this integration process to be completed (Aubépart & Franceschini, 2007).