Spatial Uncertainty Model Based on Scale-Space for RGBD-SLAM System

In the RGBD-SLAM system, the feature point's uncertainty plays an important role in back-end optimization of the entire system. By analyzing advantages and disadvantages of existing uncertainty models, we propose a scale-space-based uncertainty model. The feature point's uncertainty in disparity image space is determined simultaneously by both the scale-space where the point locates and its disparity value. The pyramid layer where the feature point locates corresponds to the uncertainty of both itself and its pixel position. And the uncertainty of the feature point's depth is also related to its disparity. That is, as feature's disparity increases, the uncertainty of the feature point's depth also increases. Compared with traditional models, our model performs better in public dataset.