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

By Thanh Mung Lam (Editor)

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Thrun, S. (2003). Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robotics and Autonomous Systems, Vol. 44, No. 1, pp. 15-27. ISSN 0921-8890. ; Usher, K. & Roberts, J. (2005). Combined optic flow and stereo-based navigation of urban canyons for a UAV. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 302-309, Edmonton. ; Konolige, K. & Bajracharya, M. (2000). Visually realistic mapping of a planar environment with stereo. In Seventh International Symposium on Experimental Robotics, Waikiki.

Simplified 2-D view After an update step where sensor data is inserted, the zones are checked for obstacles again without reference to the previous state. Since it is useful to know which specific object has been updated with the actual sensor information, the linked objects are tracked. This is done as follows: 22 Aerial Vehicles 1. 2. Calculate the center of mass of each linked object. For each linked object, try to find a matching object in the map with linked objects of the previous state. , 2000).

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. A single object is a cluster of connected occupied cells of the 3-D array. These objects are recognized with a flood fill algorithm. By saving the minimal and maximal values of the coordinates of cells belonging to the object, the bounding box is calculated and put into the global feature map as it is illustrated in figure 8. Unlike the cell array, these boxes need much less memory and it is easy to make them available to further applications, independent of the existence of a grid.

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