A robot state estimator based on multi-sensor information fusion

Mobile robot technology plays an important role in the service and manufacturing industries. The mobile robot technology includes a series of key technologies, such as robot localization, navigation and path planning. Furthermore, the state estimation problem of the robot is more critical. The state estimation of the robot mainly includes the calculation of the parameters such as the robot's position, attitude and the speed. The accuracy of the state estimation of the robot will greatly affect the working effect of the robot, including the construction of the environment map, and navigation, path planning, and so on. In recent years, a large number of scholars and experts have carried out a lot of research on the state estimation of robots. They have used various methods to estimate the state of robots. The sensors commonly used for acquiring robot state information mainly include odometers, inertial measurement units, and lasers, cameras. The characteristics of various sensors are different. Considering the complementary characteristics of sensors, this paper mainly uses multi-sensor information for estimating robot status. The extended Kalman filter is used to fuse the information coming from the camera, odometer, inertial measurement unit, and laser to estimate the robot state.