Accurate and Scalable Contour-based Camera Pose Estimation Using Deep Learning With Synthetic Data

Pose detection of objects is an important topic in object-level mapping and indoor localization. In the past, pose estimation methods were performed either with the help of artificial markers or natural features found on the object. However, due to the fact that the markers can only be utilized in controlled environment experiments, the application of marker-based approaches is very limited. Furthermore, methods that depend on the object's natural visual features require texture on the object and lack robustness to illumination and camera viewpoint variations. With the advent of Deep Learning (DL), the classical pose estimation methods have been outperformed. The DL-based pose estimation can detect deep features of the object and exhibits higher robustness to many distortions and variabilities caused by the changes in the illumination and viewpoint conditions. However, the massive training data set requirement is the main challenge with most DL-based methods. The training set is often a real set of images that have been manually labeled or annotated. In addition, such methods face problems related to the degradation of their predicted accuracy in the presence of uncertainties due to the symmetrical structure of many objects. To address the aforementioned issues, a novel and very fast method for generating synthetic data, as well as a contour-based technique for accurate pose estimation (that can handle pose ambiguities for a symmetrical object) are proposed in this paper. The tests that are conducted in multiple indoor scenarios demonstrate not only the effectiveness of the synthetic data generation but also exhibit, in many cases, the very high accuracy of the proposed pose estimation method.