基于激光视觉惯性融合，实现室外大场景实时语义地图构建
Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
- 融合激光、视觉与惯性数据，构建全局语义网格地图
- 帧处理时间小于7毫秒，支持大规模环境实时映射
- 已集成至真实导航系统，适用于校园自主导航
度量-语义地图通过编码人类先验知识，实现了对环境的高层抽象。然而，构建此类地图面临多模态传感器数据融合、实时映射性能保障以及结构与语义一致性维持等挑战。本文提出一种在线度量-语义映射系统，利用激光-视觉-惯性传感生成大规模室外环境的全局度量-语义网格地图。借助GPU加速，映射过程达到极高速度，帧处理时间始终低于7ms，不受场景规模影响。进一步地，我们将生成的地图无缝集成至真实世界导航系统中，实现基于度量-语义的地形评估与点对点自主导航。在包含24个序列的公开及自采数据集上进行了大量实验，验证了映射与导航方法的有效性。代码已开源：https://github.com/gogojjh/cobra
原文摘要 · Abstract (English)
The creation of a metric-semantic map, which encodes human-prior knowledge, represents a high-level abstraction of environments. However, constructing such a map poses challenges related to the fusion of multi-modal sensor data, the attainment of real-time mapping performance, and the preservation of structural and semantic information consistency. In this paper, we introduce an online metric-semantic mapping system that utilizes LiDAR-Visual-Inertial sensing to generate a global metric-semantic mesh map of large-scale outdoor environments. Leveraging GPU acceleration, our mapping process achieves exceptional speed, with frame processing taking less than 7ms, regardless of scenario scale. Furthermore, we seamlessly integrate the resultant map into a real-world navigation system, enabling metric-semantic-based terrain assessment and autonomous point-to-point navigation within a campus environment. Through extensive experiments conducted on both publicly available and self-collected datasets comprising 24 sequences, we demonstrate the effectiveness of our mapping and navigation methodologies. Code has been publicly released: https://github.com/gogojjh/cobra
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