对基于机器人的基础设施非破坏性评估的同时定位和绘图的审查
Ali Ghadimzadeh Alamdari1, Farzad Azizi Zade2, Arvin Ebrahimkhanlou1,3
1Department of Mechanical Engineering and Mechanics (MEM), Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA.
Sensors (Basel, Switzerland)
|February 13, 2025
概括
基于LiDAR的同步定位和绘图 (SLAM) 在封闭的环境中表现出色,在没有GPS的环境中进行基础设施检查. 结合视觉和LiDAR SLAM,提供了卓越的性能,优于单个传感器方法.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 同时定位和映射 (SLAM) 方法已经显著成熟,需要专门的审查.
- 在封闭和GPS拒绝 (EGD) 环境中进行基础设施检查对本地化和绘图提出了独特的挑战.
研究的目的:
- 审查基于视觉,基于LiDAR和混合SLAM技术的演变.
- 在EGD基础设施检查条件下评估各种开源SLAM解决方案的性能.
主要方法:
- 基于传感器融合类型和时间发展的SLAM方法的分类和详细分析.
- 使用定制和测试数据集对11个开源SLAM解决方案 (两个视觉,八个LiDAR,一个混合) 的性能进行比较.
- 评估指标包括准确性和计算资源消耗.
主要成果:
- 基于LiDAR的SLAM方法在EGD条件下表现出强的性能.
- 某些基于视觉的SLAM方法在EGD环境中显示了可接受的性能,与预期相反.
- 混合视觉-LiDAR SLAM靠一个单一的传感器类型来接近超越性能的方法.
结论:
- 激光雷达和混合SLAM对于EGD环境中的基础设施检查非常有效.
- 该研究强调了基于视觉的SLAM的潜力和传感器融合的优势,以实现强大的性能.
- 调查结果为为特定的EGD检查任务选择合适的SLAM解决方案提供了宝贵的见解.
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