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有效的基于学习的机器人导航使用基于特征的RGB-D姿势估计和拓地图

Eder A Rodríguez-Martínez^1,2, Jesús Elías Miranda-Vega³, Farouk Achakir⁴

¹Faculty of Engineering, Autonomous University of Baja California, Blvd. Benito Juárez, Mexicali 21280, Mexico.

Entropy (Basel, Switzerland)

|June 26, 2025

概括

我们开发了一个具有成本效益的RGB-D导航系统,用于使用特征匹配和轻量级神经网络的机器人. 这种方法可以在没有昂贵的传感器或大量训练数据的情况下实现可靠的室内导航.

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
人工智能的人工智能

背景情况:

强大的室内机器人导航通常需要昂贵的传感器或大型数据集.
现有的方法面临成本和数据要求的挑战.

研究的目的:

提出一个具有成本效益的RGB-D导航管道.
为了实现可靠的室内机器人导航,使用基于功能的姿势估计和轻量化政策.

主要方法:

结合基于特征的相对姿势估计与轻量级多层感知器 (MLP) 政策.
使用RGB-D关键和视觉几何约束构建拓地图.
使用LightGlue功能和SVD进行姿势估计,并使用MLP进行动作预测.
在检测到低视觉相似性或障碍时,使用Dijkstra的算法实现实时重新规划.

主要成果:

该系统在四个环境中的八个任务中实现了可靠的导航,覆盖190.44米.
代理商始终在距离目标0.1米之内停止.
在照明变化下,LightGlue展示了最大的信息获取,验证了其选择.
导航管道在商品硬件上有效运行.

结论:

拟议的模块化设计提供了可靠的导航,无需计量SLAM或大规模学习.
该系统可以适应未来的感知和政策改进.
这种方法为室内机器人导航挑战提供了具有成本效益的解决方案.

关键词:

一个RGB-D摄像机.神经网络的神经网络的神经网络点云点云是指点云.机器人导航机器人导航机器人导航拓地图是一个拓地图.视觉记忆视觉记忆视觉记忆

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