田野 , 陈宏巍 , 王法胜 , 等 . 室内移动机器人的SLAM算法综述 [J ] . 计算机科学 , 2021 , 48 ( 9 ): 223 - 234 . DOI: 10.11896/jsjkx.200700152 http://doi.org/10.11896/jsjkx.200700152 SLAM(Simultaneous Localization and Mapping),即同时定位与地图构建,目前被广泛应用于机器人领域。SLAM算法使得机器人处于陌生环境时,能够通过自身搭载的传感器来感知环境信息并建立环境地图,并完成对自身位姿的计算,从而能够在未知环境中进行移动。随着研究者们对SLAM问题的深入研究,SLAM领域相关成果已非常丰富,但是有关室内场景SLAM的论述还不够系统。通过对现有的关于SLAM算法发展成果的总结和对比,对室内SLAM进行了综合性的阐述。首先介绍了SLAM的技术现状和室内场景SLAM在不同传感器下的分类问题;其次介绍了SLAM的经典框架;然后根据相关传感器种类的不同,简要介绍了不同传感器下常见的SLAM算法的原理,同时讨论了传统室内SLAM算法中存在的诸多局限性问题,引出了基于多传感器融合技术的SLAM和基于深度学习技术的SLAM两个研究方向;最后介绍了SLAM的未来发展趋势和应用领域。

TIAN Y , CHEN H W , WANG F S , et al . Overview of SLAM algorithms for mobile robots [J ] . Computer Science , 2021 , 48 ( 9 ): 223 - 234 . (in Chinese) DOI: 10.11896/jsjkx.200700152 http://doi.org/10.11896/jsjkx.200700152 As a localization and map construction method,SLAM(Simultaneous Localization and Mapping) is widely used in the field of robots.SLAM algorithm enables the robot to perceive environmental information and establish environmental map through sensors carried by the robot itself in an unfamiliar environment,and calculate its own posture.In this way,the robot can move in an unknown environment.With the in-depth study of SLAM,the research results in the field of SLAM have been very rich.However,the discussion on indoor SLAM is not comprehensive enough.Through the summary and comparison of the exis-ting development results of the SLAM method,a comprehensive statement is shown.In this paper,the technical status of SLAM and the classification problem of SLAM under different sensors in indoor scenes are firstly introduced.Secondly,the classic framework of SLAM is revealed.Thirdly,the principles of SLAM algorithms with different sensors are described according to the different types of related sensors.Fourthly,the limitations of the traditional indoor SLAM algorithms are discussed and two research directions-SLAM based on multi-sensor fusion technology and SLAM based on deep learning technology are led out.Finally,the future development trend and application field of SLAM are suggested

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XIAO Y , ZHOU J . Overview of image edge detection [J ] . Computer Engineering and Applications , 2023 , 59 ( 5 ): 40 - 54 . (in Chinese) DOI: 10.3778/j.issn.1002-8331.2209-0122 http://doi.org/10.3778/j.issn.1002-8331.2209-0122 The task of edge detection is to identify pixels with significant brightness changes as target edges, which is a low-level problem in computer vision, and edge detection has important applications in object recognition and detection, object proposal generation, and image segmentation. Nowadays, edge detection has produced several types of methods, such as traditional gradient-based detection methods and deep learning-based edge detection algorithms and detection methods combined with emerging technologies. A finer classification of these methods provides researchers with a clearer understanding of the trends in edge detection. Firstly, the theoretical basis and implementation methods of traditional edge detection are introduced; then the main edge detection methods in recent years are summarized and classified according to the methods used, and the core techniques used in them are introduced, such as branching structure, feature fusion and loss function. The evaluation indicators used to assess the algorithm’s performance are single-image optimal threshold（ODS） and frame per second（FPS）, which are contrasted using the fundamental data set（BSDS500）. Finally, the current state of edge detection research is examined and summarized, and the possible future research directions of edge detection are prospected.

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