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在室内环境中对SLAM进行DeepLabV3+基础的语义注释改进

Shuangfeng Wei^1,2, Hongrui Tang¹, Changchang Liu¹

¹School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.

Sensors (Basel, Switzerland)

|September 19, 2025

概括

本研究介绍了针对视觉SLAM的优化DeepLabV3+框架,增强了在具有挑战性的环境中3D场景重建. 它自动化点云注释,提高机器人的效率和导航准确度.

科学领域:

机器人和计算机视觉机器人和计算机视觉
人工智能和机器学习

背景情况:

视觉SLAM系统在语义差的环境中难以进行3D重建,从而限制了机器人的效率.
语义信息的手动注释是低效的,复杂的,劳动密集的.

研究的目的:

为视觉SLAM开发一个优化的DeepLabV3+框架,整合语义细分和自动点云注释.
为了提高机器人操作效率和室内应用中的环境理解.

主要方法:

使用MobileNetV3作为DeepLabV3+的骨干,以平衡细分精度和计算负载.
引入了一个参数适应的DBSCAN算法与K-最近的邻居和KD-树加速强大的集群.
实施了动态半径值策略,以提高点云注释的完整性和精度.

主要成果:

与传统方法相比,在注释效率方面取得了显著的改进.
在点云的语义注释中保持高准确度.
证明了可靠的技术支持,以提高环境理解和导航.

结论:

拟议的框架为视觉SLAM中的语义注释提供了一个有效和准确的解决方案.
这种方法为先进的室内机器人导航和环境感知提供了至关重要的支持.

关键词:

聚类集群是指聚类的聚类.图像语义细分图像语义细分点云语义注释点云语义注释现场理解现场理解视觉上的SLAM是什么意思

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