Multi-Parameter Optimization for a Robust RGB-D SLAM System

SLAM systems can retrieve their metric scales and depth information using RGB-D cameras. However, limited by the sensing range and objects structure, RGB-D cameras can not always work well, resulting in failures sometimes. In this work, we present initialization and localization methods based on maximum-a-posteriori estimation. Our system endows monocular keypoints with valid depth values and introduce them into bundle adjustment. Depth bias coefficient and scale factor are also optimized in the local window, obtaining robustness in large scale environments and long-running operations. The experimental results indicate that our system provides the best robustness compared with other excellent methods in the literature, being able to process the most challenging sequences in the TUM RGB-D dataset.