Convolutional Neural Networks Based Active SLAM and Exploration

Mobile robots are high-performance robots that are used to perform a specific function in environments such as land, air and water, with free movement options and are equipped with many sensors for different processing capabilities. Today, it is used in many tasks such as object detection, tracking and mapping. Mobile robots used in mapping implementations are usually guided by user inputs. However, in some cases, this guidance is autonomously implemented through exploration algorithms that are examined under the active Simultaneous Localization and Mapping (SLAM) keyword. These algorithms are usually based on Laser Imaging Detection and Ranging (LIDAR) sensor. Since this sensor has a bulky structure and occupancy grid maps require heavy computing time, it is needed to develop new kinds of algorithms. In this study, we propose a novel Convolutional Neural Network (CNN) based algorithm that can create a map of an environment with a mobile robot that is independent of user inputs and move autonomously. For the first stage, the CNN structure is trained using the data set consisting of the environment image and the wheel angles related to these images so that the CNN model learns how to guide the robot. For the second stage, the robot is navigated autonomously through the trained network in an environment which is different from the first one, and the map of the environment is acquired simultaneously. Training and testing processes have been realized on a real-time implementation and the advantages of the developed method have been verified.


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