SLAM de um Robô Móvel Terrestre Utilizando Sensor LiDAR e Odometria com Filtro de Kalman Estendido

Abstract

Simultaneous Localization and Mapping (SLAM) is a research area in robotics that aims to enable a robot to build a map of an unknown environment while simultaneously estimating its own location within that map. This study proposes a solution based on the combination of a LiDAR sensor (Light Detection and Ranging) and odometry combined with an extended Kalman filter using an inertial measurement unit (IMU), with integration facilitated by the Robot Operating System (ROS). The LiDAR sensor is an optical sensor that allows the robot to measure distances and obtain information about the environment in the form of a point cloud. Odometry, on the other hand, is a technique that uses the robot's motion information to estimate its trajectory. The IMU sensor consists of accelerometers and gyroscopes, which measure linear acceleration and rotational rate of the robot. The extended Kalman filter is a statistical method that combines the information from these sensors, enabling precise estimation of the robot's position and orientation, as well as real-time mapping of the environment. The extended Kalman filter approach is used to handle the uncertainty and non-linearities present in the SLAM problem. To validate the proposed solution, experiments were conducted in a controlled environment, where the mobile ground robot was able to explore the environment, build a map, and simultaneously estimate its location. The results obtained demonstrate the effectiveness of the proposed method, providing accurate and robust performance in the SLAM task.