Mobile Robot Simultaneous Localization And Mapping Algorithms

In order to autonomous navigation, robot usually needs the map of the environment to locate its position. However, when the robot is in an unknown circumstance, there is not any map information. So, in order to locate its position, robot needs to sense and estimate the features of the environment and the robot needs to create the map of environment based on the sensed and estimated environment knowledge at the same time. This process is called simultaneous localization and mapping problem. The research content of this paper is focused on the algorithm of simultaneous localization and mapping.This paper built a simulation platform after studying the model of robot's system and integrating SLAM problem model. After this process, we study the algorithm of simultaneous localization and mapping on our built platform. This paper also analyzes EKF SLAM method and UKF SLAM method in detail, and compares the performance of EKF SLAM method with UKF SLAM such as positioning accuracy, run-time, the estimated environmental features and so on.FastSLAM considered as a hot spots method of SLAM is payed much attention in recent years. This paper gets a method called UKF FastSLAM method according to using UKF method as proposal distribution instead of using EKF method and integrating the framework of FastSLAM. At the same time, this paper also analyzes EKF SLAM method and UKF SLAM method in detail and compares the performance of FastSLAM method with UKF FastSLAM method. The experiment results show that UKF FastSLAM method is a more effective simultaneous localization and mapping method for robots.