MAPPING THREE-DIMENSIONAL ENVIRONMENTS USING RGB-D and LiDAR SENSORS APPLIED TO MOBILE ROBOTICS
ROS; Occupancy grid; SLAM; RTAB-Map; HECTOR SLAM; vSLAM; RGB-D; LiDAR; Robotics;
The autonomous action of a robot is directly related to the way it perceives the environment around it. One of the main tasks of an autonomous robot is to be able to understand the complexity of the environment in which it is inserted. This complexity is due to the shape of the objects that are present in the environment, such as tables, chairs, boxes, walls, among others. For this, sensors such as: LiDAR, RGB and RGB-D cameras are used to perceive the world outside the robot. The method that perceives the external environment is known as SLAM - Simultaneous Localization and Mapping. In SLAM, the environment is modeled using sensors and an environment map is created iteratively as the robot moves. This work addresses the mapping of three-dimensional environments on a two-dimensional map using LiDAR and RGB-D sensors. For this, the LiDAR SICK LMS111 sensor and the Microsoft Kinect RGB-D sensor coupled to the mobile terrestrial robot HUSKY A200 were used. Using GAZEBO, a 3D model from the Robotics Laboratory of the Federal University of Bahia, the 3D model of the HUSKY robot and the SICK and Kinect sensors, the SLAM algorithms were used: HECTOR SLAM and RTAB-Map to produce an occupation map two-dimensional over a fixed path. The methods were able to represent the objects contained in the environment, which for trajectory planning activity is essential to have a consistent and true to reality map.