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移动机器人视觉里程计技术研究综述

陈明方 黄良恩 王森 张永霞 陈中平

农业机械学报2024,Vol.55Issue(3):1-20,20.
农业机械学报2024,Vol.55Issue(3):1-20,20.DOI:10.6041/j.issn.1000-1298.2024.03.001

移动机器人视觉里程计技术研究综述

Survey of Research on Visual Odometry Technology for Mobile Robots

陈明方 1黄良恩 1王森 1张永霞 1陈中平1

作者信息

  • 1. 昆明理工大学机电工程学院,昆明 650500
  • 折叠

摘要

Abstract

With the continuous development of mobile robot technology,odometry technology has become a key technology for mobile robots to realize environmental perception,and its development level is of great significance to improving the autonomy and intelligence of robots.Firstly,the current development status of laser simultaneous localization and mapping(SLAM)and visual SLAM in simultaneous localization and mapping was systematically explained.The classic SLAM framework and its mathematical description were expounded,and the camera models of three common types of cameras and their mathematical description of visual odometry were briefly introduced.Secondly,the research progress of traditional visual odometry and deep learning odometry were systematically elaborated.The advantages and disadvantages of various mileage calculation methods in the past ten years were compared and analyzed.In addition,the performance of seven commonly used data sets was comparatively analyzed.Finally,the problems faced by odometry technology were summarized from the aspects of accuracy,robustness,data sets,and multi-modality,and five development trends of visual odometry were prospected from the aspects of improving the real-time performance and robustness of the algorithm.For the development of more intelligent and miniaturized new sensors,the integration with unsupervised learning,the improvement of semantic expression technology and the development of cluster robot collaboration technology were introduced.

关键词

视觉里程计/特征法/直接法/深度学习/同步定位与地图构建/数据集

Key words

visual odometry/feature method/direct method/deep learning/SLAM/dataset

分类

信息技术与安全科学

引用本文复制引用

陈明方,黄良恩,王森,张永霞,陈中平..移动机器人视觉里程计技术研究综述[J].农业机械学报,2024,55(3):1-20,20.

基金项目

国家自然科学基金项目(51965029)、国家重点研发计划项目(2018YFB1306103)和云南省重大专项(202002AC080001) (51965029)

农业机械学报

OA北大核心CSTPCD

1000-1298

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