HyMU: A Hybrid Map Updating Framework

Accurate digital map plays an important role in mobile navigation. Due to the ineffective updating mechanism, existing map updating methods cannot guarantee completeness and validity of the map. The common problems of them involve huge computation and low precision. More importantly, they scarcely consider inferring new roads on sparse unmatched trajectories. In this paper, we first address the issue of finding new roads in sparse trajectory area. On the basis of sliding window model, we propose a two-phase hybrid framework to update the digital map with inferred roads, called HyMU, which takes full advantage of line-based and point-based strategies. Through inferring road candidates for consecutive time windows and merging the candidates to form missing roads, HyMU can even discover new roads in sparse trajectory area. Therefore, HyMU has high recall and precision on trajectory data of different density and sampling rate. Experimental results on real data sets show that our proposal is both effective and efficient as compared to other congeneric approaches.