视觉定位和映射的深度学习:一项调查
IEEE transactions on neural networks and learning systems
|September 22, 2023
概括
深度学习为本地化和映射 (SLAM) 提供了一种数据驱动的方法,优于传统方法. 这项调查探讨了它对移动代理的潜力,指导未来的机器人和计算机视觉研究.
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
- 机器学习 机器学习
背景情况:
- 传统的本地化和映射依赖于手工设计的算法.
- 深度学习为这些任务提供了数据驱动的替代方案.
- 数据和计算方面的进步推动了深度学习的进步.
研究的目的:
- 调查基于深度学习的本地化和映射方法.
- 为这些新兴技术建立一个分类学.
- 评估深度学习在SLAM中的可行性和应用.
主要方法:
- 对最近文献进行了全面的审查.
- 方法的分类,包括视觉测距,重新定位和SLAM.
- 分析基于数据的方法与基于模型的方法.
主要成果:
- 深度学习显示出对准确和强大的自动运动跟踪和环境建模的重大承诺.
- 为本地化和映射中的深度学习提出了一个明确的分类学.
- 综合了来自多个AI领域的最新作品.
结论:
- 深度学习是局部化和映射的一个有希望的方向.
- 这项调查为将深度学习应用于视觉定位和映射提供了指导方针.
- 该领域正在迅速发展,整合了机器人,计算机视觉和机器学习.
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