The Deep Convolutional Neural Network Role in the Autonomous Navigation of Mobile Robots (SROBO)

作     者：Esfahlani, Shabnam Sadeghi Sanaei, Alireza Ghorabian, Mohammad Shirvani, Hassan 

作者机构：Anglia Ruskin Univ Med Technol Res Ctr MTRC Sch Engn & Built Environm Chelmsford CM1 1SQ Essex England Anglia Ruskin Univ Sch Engn & Built Environm Chelmsford CM1 1SQ Essex England 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2022年第14卷第14期

页      面：3324-3324页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

主　　题：semantic segmentation object detection autonomous navigation convolutional networks transfer learning deep learning 

摘      要：The ability to navigate unstructured environments is an essential task for intelligent systems. Autonomous navigation by ground vehicles requires developing an internal representation of space, trained by recognizable landmarks, robust visual processing, computer vision and image processing. A mobile robot needs a platform enabling it to operate in an environment autonomously, recognize the objects, and avoid obstacles in its path. In this study, an open-source ground robot called SROBO was designed to accurately identify its position and navigate certain areas using a deep convolutional neural network and transfer learning. The framework uses an RGB-D MYNTEYE camera, a 2D laser scanner and inertial measurement units (IMU) operating through an embedded system capable of deep learning. The real-time decision-making process and experiments were conducted while the onboard signal processing and image capturing system enabled continuous information analysis. State-of-the-art Real-Time Graph-Based SLAM (RTAB-Map) was adopted to create a map of indoor environments while benefiting from deep convolutional neural network (Deep-CNN) capability. Enforcing Deep-CNN improved the performance quality of the RTAB-Map SLAM. The proposed setting equipped the robot with more insight into its surroundings. The robustness of the SROBO increased by 35% using the proposed system compared to the conventional RTAB-Map SLAM.