A robust real-time indoor navigation technique based on GPU-accelerated feature matching

Robust feature tracking is a basic requisite for indoor navigation. The Scale Invariant Features Transform (SIFT) features are invariant to image translation, scaling, rotation, and partially invariant to illumination changes. Therefore, it is widely used for image matching based indoor navigation. However, the implementation of the traditional SIFT algorithm needs excessive computation operations, which is relatively time consuming and difficult for indoor navigation like real-time application. This article proposes a SIFT acceleration strategy based on graphics processing unit (GPU). It is proposed as the following four steps. Firstly, Gaussian pyramid is divided into different blocks. And in each GPU block, DoG (Difference of Gaussian) scale-space is computed. Secondly, local keypoints detection is done in GPU, and each keypoint is processed in one block to calculate gradient orientation and magnitude. Thirdly, the keypoint descriptor is formulated as vectors, and each vector is computed in one GPU block. Finally, GPU-accelerated SIFT is introduced into the indoor navigation system to address viewpoint changes. Our main contribution of this article is using an optimized GPU accelerated scheme for real-time indoor navigation. The experiments have proved that such approach can improve the computing efficiency and reduce the chances of mismatches.