Scholarlink

<p>To cater to the development market of the tourism industry, smart travel systems have been explored. Aiming at the optimization problem of navigation and positioning accuracy in the system, an optimization model based on simultaneous localization and mapping is proposed. Then, the research designs its basic system. To address the strict lighting requirements and lack of global positioning in the initial system, an inertial navigation system is introduced for optimization, and drift problems are solved through pre-integration and other strategies. Finally, global filtering sensors are used to assist in scale observation and further improve positioning accuracy. In the experimental analysis, the results showed that the absolute trajectory error and relative pose error accuracy of the research model reached 0.81&#xa0;m and 0.012&#xa0;m/s, respectively. This was on average 29.01% and 8.62% higher than the meta simultaneous localization and mapping optimization model and the inertial navigation system optimization model. In summary, the composite optimization model designed in the study has the best navigation and positioning effect. It can provide certain optimization and reference significance for smart tourism systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimized visual front-end navigation technology based on INS SLAM in smart tourism systems

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月27日

查看全文
  • Cheng Pan,
  • Yuqing Liu,
  • Meijiao Sun

摘要

To cater to the development market of the tourism industry, smart travel systems have been explored. Aiming at the optimization problem of navigation and positioning accuracy in the system, an optimization model based on simultaneous localization and mapping is proposed. Then, the research designs its basic system. To address the strict lighting requirements and lack of global positioning in the initial system, an inertial navigation system is introduced for optimization, and drift problems are solved through pre-integration and other strategies. Finally, global filtering sensors are used to assist in scale observation and further improve positioning accuracy. In the experimental analysis, the results showed that the absolute trajectory error and relative pose error accuracy of the research model reached 0.81 m and 0.012 m/s, respectively. This was on average 29.01% and 8.62% higher than the meta simultaneous localization and mapping optimization model and the inertial navigation system optimization model. In summary, the composite optimization model designed in the study has the best navigation and positioning effect. It can provide certain optimization and reference significance for smart tourism systems.