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Simultaneous Localization and Mapping(SLAM) is the most important tool in creating map and auto-navigation, which is an indispensable link for pilotless automobile. The current SLAM algorithms suffer from unreliable feature matching and large registration error. To reduce those deficiencies, we proposed a key-frame estimation and local map upgrade scheme, which include the following 3 parts:1) Local map matching strategy; 2) Local map updating scheme; 3) Key-frame selecting scheme. Experimental results proved that our scheme improved the performance of current localization methods.

A New SLAM Algorithm with Key-Frame Estimation and Local Map Upgrade Scheme