摘要
同步定位与地图构建技术(SLAM)是当前机器人领域的重要研究热点,传统的SLAM技术虽然在实时性方面已经达到较高的水平,但在定位精度和鲁棒性等方面还存在较大缺陷,所构建的环境地图虽然一定程度上满足了机器人的定位需要,但不足以支撑机器人自主完成导航、避障等任务,交互性能不足.随着深度学习技术的发展,利用深度学习方法提取环境语义信息,并与SLAM技术结合,越来越受到学者的关注.本文综述了环境语义信息应用到同步定位与地图构建领域的最新研究进展,重点介绍和总结了语义信息与传统视觉SLAM在系统定位和地图构建方面结合的突出研究成果,并对传统视觉SLAM算法与语义SLAM算法做了深入的对比研究.最后,展望了语义SLAM研究的发展方向.
The simultaneous localization and mapping(SLAM)technique is an important research direction in robotics.Although the traditional SLAM has reached a high level of real-time performance,major shortcomings still remain in its positioning accuracy and robustness.Using traditional SLAM,a geometric environment map can be constructed that can satisfy the pose estimation of robots.However,the interactive performance of this map is insufficient to support a robot in completing self-navigation and obstacle avoidance.One popular practical application of SLAM is to add semantic information by combining deep learning methods with SLAM.Systems that introduce environmental semantic information belong to semantic SLAM systems.Introduction of semantic information is of great significance for improving the positioning performance of a robot,optimizing the robustness of the robot system,and improving the scene-understanding ability of the robot.Semantic information improves recognition accuracy in complex scenes,which brings more optimization conditions for an odometer,pose estimation,and loop detection,etc.Therefore,positioning accuracy and robustness is improved.Moreover,semantic information aids in the promotion of data association from the traditional pixel level to the object level so that the perceived geometric environmental information can be assigned with semantic tags to obtain a high-level semantic map.This then aids a robot in understanding an autonomous environment and human–computer interaction.This paper summarized the latest researches that apply semantic information to SLAM.The prominent achievements of semantics combined with the traditional visual SLAM of localization and mapping were also discussed.In addition,the semantic SLAM was compared with the traditional SLAM in detail.Finally,future research topics of advanced semantic SLAM were explored.This study aims to serve as a guide for future researchers in applying semantic information to tackle localization and mapping problems.
作者
李小倩
何伟
朱世强
李月华
谢天
LI Xiao-qian;HE Wei;ZHU Shi-qiang;LI Yue-hua;XIE Tian(Zhejiang Lab,Hangzhou 311100,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2021年第6期754-767,共14页
Chinese Journal of Engineering
基金
国家重点研发计划资助项目(2018AAA0102703)
科工局稳定支持项目(HTKJ2019KL502005)
第67批中国博士后科学基金面上资助项目(HTKJ2019KL502005)。
