A study of a soft computing based method for 3D scenario reconstruction

Several recent works deal with 3D data in mobile robotic problems, e.g., mapping. Data comes from any kind of sensor (time of flight, Kinect or 3D lasers) that provide a huge amount of unorganized 3D data. In this paper we detail an efficient approach to build complete 3D models using a soft computing method, the Growing Neural Gas (GNG). As neural models deal easily with noise, imprecision, uncertainty or partial data, GNG provides better results than other approaches. The GNG obtained is then applied to a sequence. We present a comprehensive study on GNG parameters to ensure the best result at the lowest time cost. From this GNG structure, we propose to calculate planar patches and thus obtaining a fast method to compute the movement performed by a mobile robot by means of a 3D models registration algorithm. Final results of 3D mapping are also shown.This work has been supported by grant DPI2009-07144 from Ministerio de Ciencia e Innovacion of the Spanish Government, by the University of Alicante’s projects GRE09-16 and GRE10-35 and Valencian Government project GV/2011/034

ステレオ視方式三次元距離センサーLSIの高性能化に関する研究

九州工業大学博士学位論文 学位記番号:情工博甲第302号 学位授与年月日:平成27年3月25日第1章 序論|第2章 三次元センサーによる距離検知技術|第3章 三次元距離センサーLSIの高集積化|第4章 相関信号鮮明化機能搭載三次元距離センサーLSI|第5章 広ダイナミックレンジイメージセンサー搭載三次元距離センサーLSI|第6章 距離検知精度向上三次元距離センサーLSI|第7章 総括九州工業大学平成26年