在Jetson Nano上实现视觉口径测量
Jakub Krško1, Dušan Nemec1, Vojtech Šimák1
1Department of Control and Information Systems, FEIT, University of Zilina, 010 26 Zilina, Slovakia.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究在Jetson Nano上实现ORB-SLAM3用于机器人视觉测距,通过低功耗系统实现精确的定位. 讨论了现实世界移动机器人跟踪的挑战和错误来源.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 嵌入式系统 嵌入式系统
背景情况:
- 视觉测距对于机器人导航至关重要.
- 在低功耗系统上实施先进的算法带来了独特的挑战.
- ORB-SLAM3提供了强大的视觉SLAM功能.
研究的目的:
- 在基于ARM的低功耗系统 (Jetson Nano) 上实现和评估ORB-SLAM3用于视觉测距.
- 评估ORB-SLAM3在各种环境中的移动机器人跟踪的准确性和局限性.
主要方法:
- 适应ORB-SLAM3算法用于ARM架构.
- 系统优化,包括软件库选择和摄像头校准.
- 使用EuRoC数据集和现实世界移动机器人实验进行测试.
主要成果:
- 实现了精确的定位,在EuRoC数据集上路径估计误差在3-11厘米之间.
- 现实世界的测试发现了由于编码器漂移和环境因素 (照明,纹理) 的差异.
结论:
- 在嵌入式系统上,ORB-SLAM3可用于低功耗视觉测距.
- 对于现实世界的应用,增强校准和传感器融合等错误缓解策略是必要的.
- 未来的工作包括轨迹校正和改进与其他机器人系统的集成.
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