Filtering 3D Keypoints Using GIST For Accurate Image-Based Localization

Image-Based Localization (IBL) is the problem of estimating the 3D pose of a camera with respect to a 3D representation of the scene. IBL is quite challenging in largescale environments spanning a wide variety of viewpoints, illumination, and areas where matching a query image against hundreds of thousands of 3D points becomes prone to a large number of outliers and ambiguous situations. The current state of the art IBL solutions attempted to address the problem using paradigms such as bag-of-words, features co-occurrence, deep learning and others, with varying degrees of success. This paper presents GIST-based Search Space Reduction (GSSR) for indoor and large scale Image-Based Localization applications such as relocalization, loop closure and location recognition. GSSR explores the use of global descriptors, in particular GIST, to introduce a new similarity measure for keyframes that combines the GIST descriptor scores of all neighboring frames to qualify a limited number of 3D points for the matching process, hence reducing the problem to its small size counterpart. Our results on standard datasets show that our system can achieve better localization accuracy and speed than the main state of the art. It obtains approximately 0.24m and 0.3◦ in less than 0.1 seconds.