RGB-D Deep Learning keypoints and descriptors extraction Network for feature-based Visual Odometry systems

Sammanfattning: Feature extractors in Visual Odometry pipelines rarely exploit depth signals, even though depth sensors and RGB-D cameras are commonly used in later stages of Visual Odometry systems. Nonetheless, depth sensors from RGB-D cameras function even with no external light and can provide feature extractors with additional structural information otherwise invisible in RGB images. Deep Learning feature extractors, which have recently been shown to outperform their classical counterparts, still only exploit RGB information. Against this background, this thesis presents a Self-Supervised Deep Learning feature extraction algorithm that employs both RGB and depth signals as input. The proposed approach builds upon the existing deep learning feature extractors, adapting the architecture and training procedure to introduce the depth signal. The developed RGB-D system is compared with an RGB-only feature extractor in a qualitative study on keypoints’ location and a quantitative evaluation on pose estimation. The qualitative evaluation demonstrates that the proposed system exploits information from both RGB and depth domains, and it robustly adapts to the degradation of either of the two input signals. The pose estimation results indicate that the RGB-D system performs comparably to the RGB-only one in normal and low-light conditions. Thanks to the usage of depth information, the RGB-D feature extractor can still operate, showing only limited performance degradation, even in completely dark environments, where RGB methods fail due to a lack of input information. The combined qualitative and quantitative results suggest that the proposed system extracts features based on both RGB and depth input domains and can autonomously transition from normal brightness to a no-light environment, by exploiting depth signal to compensate for the degraded RGB information.