A Study on Automatic 3-D House-layout Construction by a Vision-based Autonomous Vehicle Using a Two-camera Omni-directional Imaging System

• Main Authors: You, Bo-Jhih, 尤柏智
• Other Authors: Tsai, Wen-Hsiang
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/34150558968895307404

碩士 === 國立交通大學 === 多媒體工程研究所 === 98 === An automatic house-layout construction system via vision-based autonomous vehicle navigation without human involvement is proposed. The system can be used to acquire the floor layout of an empty room, which consists of mutually perpendicular wall lines as well as shapes of flat objects like doors and windows. To achieve acquisition of environment images, a new type of omni-directional camera is designed, which consists of two omni-cameras aligned coaxially and back to back, with the upper and lower cameras taking respectively images of the upper and lower semi-spherical spaces of the environment. The pano-mapping approach is adopted to compute 3-D depth data of space feature points using the upper and lower omni-images taken by the proposed imaging system. The proposed automatic house layout construction process consists of three stages, namely, vehicle navigation by mopboard following, floor layout construction, and 3-D graphic house layout display. In the first stage, vehicle navigation is conducted to follow the wall mopboards in a house. An image processing scheme is applied directly on the omni-image to extract mopboard points. Then, a pattern classification technique is proposed to classify the points in two perpendicular groups, which are then fitted by lines using an LSE criterion. Such mopboard edge lines are used to represent the walls including the mopboards. In the second stage, a global optimization method is proposed to construct a floor layout from all the mopboard edge lines in a sense of minimizing the total line fitting error. And in the last stage, doors and windows are detected in an offline fashion from the omni-images taken in the navigation session. The data of the detected door and window then are merged into the mopboard edge data to get a complete 3-D data set as the house structure, which finally is transformed into a graphic form for 3-D display from any viewpoint. The experimental results show that the constructed floor layout is precise enough with an approximate average error of 3%, and that automatic vehicle navigation may be repeated successfully to construct graphic house layouts. These facts show the feasibility of the proposed approach.