Frame Stitching in Indoor Environment Using Drone Captured Images

Abstract

Drones are used in a number of industrial applications such as asset tracking and inspection. Indoor industrial applications based on visual data pose various challenges such as low lighting conditions and presence of non-planar scenes. Due to the nature of the indoor applications, image data is acquired at close range and this leads to the loss of context. In order to get global context, image stitching is a key step for data interpretation. We propose an approach to stitch drone-captured indoor video frames, where feature based stitching fails. In order to achieve this, the image feature data extracted is fused with drone inertial measurement unit (IMU) data. The approach is tested in a warehouse and the performance is compared with other state-of-the-art image stitching algorithms. The proposed approach shows robust performance in cases of highly non-planar scenes.