Image feature point matching for indoor positioning

With the development of technology and the popularity of smart phones, people have become more considerate about the convenience in life of the disabilities and thus add lots of assistive equipment. Portable equipment such as smart phones and tablet are daily necessities for most of the people. However, there are few applications that are designed for the visual impairments and the elders. The proposed system can be used indoor to get the location of the user and plan the path. Via matching the image features, it will recognize where the user is. The purpose of this is to help the visual impairments or the elders to navigate to their destinations. In addition, it can also use robot to get the position by matching image features. The proposed system uses images captured from smart phones and are compared with database, then it will return the most similar position back to the user. User then can obtain some useful information of the surroundings such as the location of toilet or elevator, which will help user planning the path to destinations. Scale-invariant feature transform (SIFT) is used in this thesis. Via image features matching with pre-established scenes image features database. FLANN (Fast Library for Approximate Nearest Neighbors) is applied to build randomized k-d trees. The k-d tree can create an index for SIFT’s descriptor, which can speed up feature matching. Experimental results in the proposed method can achieve a good feasbilitu.