Simultaneous Localization And Mapping Based On Multiple Sensors

In the research of modern robot, the research of the autonomous mobile robot has occupied a very important position. In order to realize the robot’s autonomous movement, it must have the ability to perceive the environment and realize the self-positioning. It is necessary to have the ability to construct the map. However, the self-localization of the robot must have a precise environment map, but the environment map is also a supplement to the process. Therefore, it is necessary to construct the robot’s self-motion as a whole. In this paper, we have to study the related technologies and the implementation of the simultaneous localization and mapping of the robot:In this paper, the robot motion model is established, and the position information of the robot is determined by the internal sensor of the robot. However, in the movement process of the robot, the error of the angle of the gyroscope and the wheel slip is more and more big, and the results of its location and the effect of the construction of the map will become worse and worse with the increase of the mileage. Therefore, in order to improve the accuracy of the robot’s localization and map accuracy, this paper uses a combination of gyroscope, mileage meter, Bluetooth node and laser sensors to detect and map building method: robot through the Bluetooth module installed in the mobile environment to detect the Bluetooth module in the mobile environment, the signal value(RSSI) of the transmitter module to calculate the distance between the robot and the Bluetooth transmitter module. The most important problem of the combination of multiple sensors and the construction of the map method is the problem of multi-sensor data fusion. In this paper, we study the indoor positioning of the robot, we use the Extended Kalman Filter algorithm(EKF) to achieve multiple sensors data fusion, and specifically discusses how to achieve the expansion of Kalman Filter, and experiments, the use of Extended Kalman Filter algorithm for the experimental data collected in the fusion of sensor data to verify the effect of the Extended Kalman Filter algorithm in robot localization.In this paper, the Bluetooth module has a large limitation in the path planning of the robot, and the grid map is conducive to the map creation and maintenance. In the process of the laser scanning to the original obstacle data points, we use the arithmetic mean filter to filter the obstacle data points, and then draw the image processing method, which can make the obstacle contour can be displayed more clearly. In the same way, we construct the map to verify the validity of the map using the method.