RGB-D SLAM with Deep Depth Completion.

RGB-D indoor mapping has been an active research topic in the last decade with the release of various depth sensors. Researchers proposed impressive SLAM systems such as ORB-SLAM2. However, the depth sensors are sensitive to illumination conditions and have limited range, which lead to missing or invalid depth data. This situation negatively affects the performance of RGB-D SLAM systems. Moreover, deep learning based approaches for estimating depth data from color frames have been proposed recently. Therefore, in this study, we aim to analyze deep depth estimation performance on SLAM. We propose a depth completion approach which merges sensor depth data and the estimated depth. To do this, we integrate a deep depth estimation method into a state-of-the-art indoor RGB-D SLAM system. The experimental results show that the proposed depth completion approach improves mapping performance.