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Abstract
Autonomous mobile robots applications require a robust navigation system, which ensures the proper movement of the robot while performing their tasks. The key challenge in the navigation system is related to the indoor localization. Simultaneous Localization and Mapping (SLAM) techniques combined with Adaptive Monte Carlo Localization (AMCL) are widely used to localize robots. However, this approach is susceptible to errors, especially in dynamic environments and in presence of obstacles and objects. This paper presents an approach to improve the estimation of the indoor pose of a wheeled mobile robot in an environment. To this end, the proposed localization system integrates the AMCL algorithm with the position updates and corrections based on the artificial vision detection of fiducial markers scattered throughout the environment to reduce the errors accumulated by the AMCL position estimation. The proposed approach is based on Robot Operating System (ROS), and tested and validated in a simulation environment. As a result, an improvement in the trajectory performed by the robot was identified using the SLAM system combined with traditional AMCL corrected with the detection, by artificial vision, of fiducial markers.
Highlights
Mobile robots are widespread in several areas, such as industrial automation, agriculture, medical care, autonomous driving, product deliveries, planetary exploration, smart warehouses, personal services, construction, reconnaissance, entertainment, emergency rescue operations, patrolling and transportation [1]
Systems with time-of-flight based signals, such as POZYX, have a good position result, they do not obtain a satisfactory orientation [6]. Another adopted method for indoor location is the Simultaneous Localization and Mapping (SLAM), which was introduced to enable robots to generate maps of their surroundings based on LiDAR information, which is used for robot position estimation as well as the path planning between different points in the environment [7]
When there are no position updates being performed by the tag detection, the Adaptive Monte Carlo Localization (AMCL) algorithm uses the data received from the LiDAR sensor readings and the robot’s odometry, in conjunction with the map data generated by Gmapping through a Robot Operating System (ROS) service called map server, to perform the particle filtering, estimating the localization of the robot in the environment
Summary
Mobile robots are widespread in several areas, such as industrial automation, agriculture, medical care, autonomous driving, product deliveries, planetary exploration, smart warehouses, personal services, construction, reconnaissance, entertainment, emergency rescue operations, patrolling and transportation [1]. Systems with time-of-flight based signals, such as POZYX, have a good position result, they do not obtain a satisfactory orientation [6] Another adopted method for indoor location is the Simultaneous Localization and Mapping (SLAM), which was introduced to enable robots to generate maps of their surroundings based on LiDAR information, which is used for robot position estimation as well as the path planning between different points in the environment [7]. Even when performing SLAM with the AMCL algorithm, the robot’s position estimation can be subject to errors This discrepancy between the real and estimated position increases when the scenario has dynamic obstacles, which ends up constantly changing the laser sensor readings, or hallways that are very long and very similar in their extension, making difficult to determine the actual position of the robot in the environment [9].
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