Aerial Vehicles (2009) by Thanh Mung Lam (Editor)

By Thanh Mung Lam (Editor)

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Figure 7 illustrates how the zone partitioning works. The actual camera and local map position is shown in figure 7a, the other graphics show possible effects on the map, caused by the next measurement. Often, rotations (7b) or movements (7c) will not have an effect on the zone boundaries. But if the movement is larger so that boundaries are crossed (7d), new memory is allocated for these zones. Grid information is discarded for zones that fall outside the immediate vicinity. 5. 1 Determining Separate Objects and Bounding Boxes Object features are detected by segmenting the global map into occupied and free areas applying a threshold.

This scheme is composed of two parts. In first, all functions that we would integrate into FPGA (blue, green and red functions). Secondly, hovercraft dynamics, lens/photoreceptors system and visual environments simulate the trajectories resulting from the LORA dual regulator scheme (Cyan and cyan hatched functions). These ‘high level behavioural models’ will be used when digital, timing and architecture specifications will be defined for the functions to integrate. Figure 5. LORA III functional block diagram.

10). It is possible to include a-priori knowledge about obstacles here. Combining Occupancy Grids with a Polygonal Obstacle World Model for Autonomous Flights (a) 21 (b) Figure 9. Vehicle movement from (a) to (b). Features are stored when the grid data is discarded due to helicopter movement (a) (b) Figure 10. Vehicle movement from (a) to (b). 3 Merging and Tracking Obstacles Single objects found in the temporary grid zones are limited to the zone boundaries since each zone is processed separately.

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