Indoor Localization And Navigation Control Strategies For A Mobile Robot Designed To Inspect Confined Environments

Localization is critical for autonomous robot operation, and selecting a suitable method for pose estimation is still a challenging task. In this sense, this paper investigates different localization and navigation control strategies deployed into the EspeleoRobo, a robotic platform designed by the Brazilian mining company Vale S.A. to inspect confined areas. We compare the pose estimation algorithms based on wheel, visual and LiDAR odometry, and also Ultra-Wideband radio signals, all fused with IMU (Inertial Measurement Unit) data. Our experiments consider both teleoperated and autonomous robot operation. The robot's autonomous navigation is based on an artificial vector fields controller, which uses the different pose estimations as feedback to guide the robot through pre-defined paths. Real experiments performed in indoor environments illustrate the performance of each estimator. Finally, preliminary mapping results states for the LiDAR SLAM (Simultaneous Localization and Mapping) approach as a promising option for practical field operations.