CEB-Map: Visual Localization Error Prediction for Safe Navigation

For safe visual navigation, areas with high localization errors should be concentrated and could be further refined by additional mapping operations. Given an environment map, we propose to predict the visual localization error and hence to either improve the navigation performance or call an additional mapping to refine the built map. Previous work adopts the uncertainty of landmarks for the error prediction. In our work, we take into account both the spatial distribution of visual landmarks and the uncertainty of landmarks. Our main idea is that standing at one place, a good spatial distribution of landmarks means a sufficient enough visible landmarks from all views at that place, i.e., enough landmarks under arbitrary view-direction. Combining the spatial distribution and the uncertainty of landmarks, we propose a new framework to predict the error of visual localization. Furthermore, we show that additional mapping in the area with high predicted error can significantly improve the visual localization precision. Experimental results show that there is a strong relationship between the predicted error and the real error, of which the absolute value of correlation coefficient is between 0.707 to 0.915. We apply our method to conduct an optimal refining policy on the built map and the experimental results show the improved localization precision. Applications on navigation test verify the superiority of our proposed method.

Automated summary

This study introduces a new method to predict localization errors by considering both the spatial distribution and uncertainty of visual landmarks, and further improve navigation accuracy by additional mapping. Experimental results demonstrate a strong correlation between predicted and actual errors, showing significant enhancement in localization precision with the proposed approach.