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Additional resources for Aerial Vehicles (2009)
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. In IEEE International Conference on Robotics and Automation, pp. 2120-2125, Seoul. ; Scholz, J. & Elfes, A. (2000).
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. 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.
Robot motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research, Vol. 17, No. 7, pp. 760772. ISSN 0278-3649. ; Laugier, C. (2007). Dynamic obstacle avoidance in uncertain environment combining PVOs and occupancy grid. In IEEE International Conference on Robotics and Automation, pp. 1610-1616, Roma. ; Beard, R. (2007). Obstacle and terrain avoidance for miniature aerial vehicles. Valavanis, K. P. ), Advances in Unmanned Aerial Vehicles, pp. 213-244.