Reduced-complexity data acquisition system for image-based localization in indoor environments
Image-based localization has important commercial applications such as augmented reality and customer analytics. In prior work, we developed a three step pipeline for image-based localization of mobile devices in indoor environments. In the first step, we generate a 2.5D georeferenced image database using an ambulatory backpack-mounted system originally developed for 3D modeling of indoor environments. Specifically, we first create a dense 3D point cloud and polygonal model from the side laser scanner measurements of the backpack, and then use it to generate dense 2.5D database image depthmaps by raytracing the 3D model. In the second step, a query image is matched against the image database to retrieve the best-matching database image. In the final step, the pose of the query image is recovered with respect to the best-matching image. Since the pose recovery in step three only requires sparse depth information at certain SIFT feature keypoints in the database image, in this paper we improve upon our previous method by only calculating depth values at these keypoints, thereby reducing the required number of sensors in our data acquisition system. To do so, we use a modified version of the classic multi-camera 3D scene reconstruction algorithm, thereby eliminating the need for expensive geometry laser range scanners. Our experimental results in a shopping mall indicate that the proposed reduced complexity sparse depthmap approach is nearly as accurate as our previous dense depth map method.