硬件实现了改进的定向快速和旋转简报-同时定位和映射版本2的硬件实现
Ji-Long He1, Ying-Hua Chen2, Wenny Ramadha Putri2
1School of Computer Science and Technology, Shandong University of Technology, Zibo 255000, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究增强了ORB-SLAM2算法,以在像Raspberry Pi 3这样的资源有限的设备上实现高效的自主导航. 改进后的系统在同时定位和映射 (SLAM) 中实现了更高的准确性,同时保持了实时性能.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 自主系统工程 自主系统工程
背景情况:
- 在自动驾驶中实现高水平的自动化需要准确的映射,特别是在未知的环境中.
- 同时定位和映射 (SLAM) 算法对于导航至关重要,但往往需要大量的计算能力.
- 现有的SLAM方法在资源有限的平台中难以实现效率.
研究的目的:
- 增强ORB-SLAM2算法,以提高树派3上的映射性能.
- 为应对嵌入式系统在SLAM中平衡精度和计算效率的挑战.
- 在具有有限计算资源的环境中实现实时自主导航.
主要方法:
- 在Raspberry Pi 3 (ARM A53 CPU) 上实现一个增强的ORB-SLAM2算法.
- 优化功能描述符,以提高立体声功能匹配率.
- 调整闭环参数以最大限度地减少映射中的累积错误.
- 使用机器人操作系统 (ROS) 进行系统集成和性能监控.
主要成果:
- 在单眼SLAM中显著减少错误:RMSE下降了18.11%,平均错误为22.97%,中位数错误为29.41%,最大错误为17.18%.
- 立体SLAM中小误差的减少:RMSE下降了0.30%,平均误差减少了0.38%.
- 稳定的ROS主题频率为10Hz,四核CPU利用率约为90%,表明实时能力.
结论:
- 增强的ORB-SLAM2系统为资源有限的平台提供了准确和高效的实时SLAM的可行解决方案.
- 优化成功地提高了映射精度,同时保持了嵌入式自主系统的计算可行性.
- 这项工作展示了一种实际的方法,用于在自动驾驶边缘计算场景中部署先进的SLAM功能.
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