文献类型：期刊文献

英文题名：Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey

作者：Tang, Yang[1];Zhao, Chaoqiang[1];Wang, Jianrui[1];Zhang, Chongzhen[2];Sun, Qiyu[1];Zheng, Wei Xing[3];Du, Wenli[1];Qian, Feng[1];Kurths, Juergen[4,5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Shanghai AI Lab, Shanghai 200030, Peoples R China;[3]Western Sydney Univ, Sch Comp Data & Math Sci, Sydney, NSW 2751, Australia;[4]Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany;[5]Humboldt Univ, Inst Phys, D-12489 Berlin, Germany

年份：2023

卷号：34

期号：12

起止页码：9604

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20221912092350);WOS:【SCI-EXPANDED(收录号:WOS:000788955300001)】；

基金：This work was supported in part by the National Natural Science Foundation of China (Basic Science Center Program) under Grant 61988101, in part by the National Natural Science Fund for Distinguished Young Scholars under Grant 61725301, in part by the Shanghai Artificial Intelligence Laboratory, in part by the Program of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, in part by the Program of Shanghai Academic Research Leader under Grant 20XD1401300, in part by the China National Petroleum Corporation (CNPC) Innovation Fund under Grant 2021D002-0902, and in part by the Shanghai Artificial Intelligence Laboratory. (Yang Tang and Chaoqiang Zhao contributed equally to this work). (Corresponding author: Yang Tang.)

语种：英文

外文关键词：Autonomous systems; Navigation; Learning systems; Deep learning; Visualization; Simultaneous localization and mapping; Sensors; Autonomous system; deep learning; environment perception; learning systems; navigation; reinforcement learning

摘要：Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.