Research On Simultaneous Localization And Mapping Of Sub-system In Mobile Robot

In the field of robotics research, autonomous mobile robotics research is a significant area of robot research branch. in order to achieve autonomous mobile, robots need to be able to understand the perception of the environment and to achieve self-positioning, which is the ability to have simultaneous localization and mapping. However, the robot achieves accurate positioning requires the environment map, and environment map cannot leave accurate positioning. this is contradictory but interrelated processes, to achieve the autonomous mobile robot must solve simultaneous localization and map building as a problem. This paper focuses on simultaneous localization and mapping related technologies of mobile. The main work includes:This paper established a model of the robot navigation, in or der to get location information, by using odometer to determine of the robot to achieve the location information. Since the robot wheels slip and other reasons that error of the odometer accumulate, localization and mapping effect will be deteriorated with the growth route. In order to improve the accuracy of localization and mapping, this paper introduces a method which combine of odometer and reference signs landmark feature of simultaneous localization and maping. This paper introduces a combination of odometer and reference landmarks simultaneously localization and mapping method. Using the cameras of robot to identify landmark, when it detects a new landmark, the landmark is added to landmark feature map. If the landmark is already in the landmark feature map. according to the relative position of observed landmark and the robot,and merge the data of the odometer to correct location information and update landmark feature map.This key issue of combining data from multiple sensors for motion control is the data fusion problem. For features of the system,the paper chose EKF algorithm and gives a detailed process of EKF-SLAM and achieves a simulation, simulation results show the effectiveness of EKF-SLAM.Due to the large limitations of landmark feature maps on the robot path planning,the grid map has the advantage of easy to path planning and easy to create and maintain. Finally, this paper describes how to create probabilistic grid maps based on EKF-SLAM precise positioning, using Bayes’ rule thought to resolve the conflict when need to integrate the information from multiple ultrasonic sensors,using data collected by the ultrasonic sensor to change the probability of the grid is occupied by obstacles,integrate the local map, re-using Bayes’ rule to update the global grid map. The simulation proved the validity and accuracy of the method to create grid map.