Robot Localization Using RE and Inertial Sensors

Abstract

A mobile robot must know its position in order to operate autonomously. The process of determining the robot's absolute position from sensor data is called robot localization. Sonar, inertial, RF, and laser sensors can all be used for navigation and localization purposes. These sensors can achieve good accuracy when operating in certain conditions. For example, sonar is useful when operating in a mapped environment containing known obstacles. Inertial sensors have trouble with drift, which is accentuated when moving continuously for long periods of time. By merging the results from multiple sensors, the accuracy over a wider range of conditions can be obtained. This work proposes a technique of merging heterogeneous signals from inertial and RF sensors. Since sensors have errors associated with their readings, the robot's state will be represented probabilistically. Based on the sensors used in this work, the robot's position, velocity, and acceleration will be estimated using a joint probability distribution function (PDF). At each time step, this PDF will be updated based on the RF readings and then updated again based on the readings from the inertial sensor. The proposed algorithm will be applied to simulation of an uncluttered, level environment. The accuracy of the localization algorithm is compared to the accuracies obtained by other localization algorithms. The results show better localization accuracy when using the RF and inertial sensors together.