A Smart Post-Rectification Algorithm Based on an ANN Considering Reflectivity and Distance for Indoor Scenario Reconstruction

Indoor scene reconstruction is important for robot positioning and navigation in scenario reconstruction, especially in constructing a semantic map. In previous research, RGB-D cameras have been utilized to obtain a semantic map. However, because of indoor objects and depth sensors, the accuracy and precision of the depth values could be improved, which is a key factor in reconstructing indoor scenarios. Moreover, there is a relationship between reflectivity and depth accuracy. Therefore, to obtain depth information that is better than that obtained in our previous research, we present a smart post-rectification algorithm based on an artificial neural network (ANN). The algorithm improves the accuracy and precision of depth values by simultaneously considering reflectivity, distances, and different mechanisms of measuring depth. First, we analyze the RGB-D cameras’ characteristics, including the pinhole camera model, lens distortions, and the types of error factors due to the types of RGB-D cameras used. Then, this paper proposes a smart post-rectification algorithm for depth images based on an ANN considering the depth error caused by reflectivity, the distance-related depth error, and different mechanisms for measuring depth. Finally, we perform experiments to evaluate the accuracy and precision of the proposed post-rectification approach by using different types of depth sensors. To evaluate the performance of our proposed algorithm, the proposed approach is applied to RGB-D SLAM, which is tested in different indoor environments. The experimental results show that applying our post-rectification algorithm to indoor scenario reconstruction can result in more accurate and more detailed 3-D reconstruction of objects than other state-of-the-art methods, highlighting the robustness and efficiency of our proposed algorithm.