Abstract
SLAM (Simultaneous Localization And Mapping) is considered a fundamental problem for robots to become truly autonomous, and it is one of the most popular topic in the field of mobile robotics. When robot works in a unknown environment, it should estimate the current position relative to the environment and meanwhile estimate the environment. When both localization and mapping must be solved concurrently, the problem is called SLAM. SLAM can be implemented in many ways such the Particle Filter, Extended Kalman Filter and Graph-based solution. Currently, one of the most widely used algorithms to solve SLAM is Graph-based solution. In this paper we present a method for robot to calculate its accurate location in indoor environment using graph based optimization. We describe a way how to extract feature from laser range data and how to associate the features, and construct a robot pose graph when robot move in 2D environment. In the last of the paper, we present two simulated robot pose graph to compare the optimization result. The experimental results demonstrate our graph based optimization method is workable.
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