The Application of SLAM Based on Extended Kalman Filteration

In this paper, there is a brief introduction about Kalman filteration. Then, analyze the working principle of Simultaneous Localization and Mapping, also called Concurrent Localization and Mapping. Give an introduction about different types of SLAM and the sensors required. After then, give some applications to the SLAM. A practical solution of laparoscopic sequence for surgery, which applied some feature that occur in the realistic use and overcome these obstacles. After then introduce a optimizing method for SLAM to be applied to embedded systems, using coprocessor to solve the calculation problem. Then is another algorithm that can be applied with SLAM to adapt different environments in space, which can automatically adjust the output voltage of different component to meet the scarcity of resources in space. Also introduce an application of algorithm to split the mapping of large scale to small scale map, which can increase the smoothness for closing loop route. Then suit the need for realistic world, give conclusion about different way of implementing multiple robots SLAM problem.