对比三类SLAM在嵌入式设备上的表现，找高效实用方案
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey
- 分几何、神经辐射场、3D高斯泼溅三类架构对比
- 高精度方法耗能大，几何类在资源受限下更优
- 适合想部署轻量语义SLAM的开发者参考
在嵌入式系统中，机器人需高效感知并理解环境以在真实场景中可靠运行。视觉语义SLAM通过将语义信息融入地图，提升决策能力。但在资源受限硬件上实现时，需权衡精度、计算效率与功耗。本文对近期视觉语义SLAM方法进行对比综述，重点关注其在嵌入式平台的适用性。分析几何SLAM、神经辐射场（NeRF）和3D高斯泼溅三类架构，在NVIDIA Jetson AGX Orin上的表现，评估其精度、分割质量、内存占用与能耗。结果表明，基于NeRF和高斯泼溅的方法虽具备高语义细节，但计算需求大，难以在嵌入式设备上使用；而语义几何SLAM在计算成本与精度间取得更优平衡。研究指出需开发更适配嵌入式环境的算法，并倡导通过软硬件协同设计提升效率。
原文摘要 · Abstract (English)
In embedded systems, robots must perceive and interpret their environment efficiently to operate reliably in real-world conditions. Visual Semantic SLAM (Simultaneous Localization and Mapping) enhances standard SLAM by incorporating semantic information into the map, enabling more informed decision-making. However, implementing such systems on resource-limited hardware involves trade-offs between accuracy, computing efficiency, and power usage. This paper provides a comparative review of recent Semantic Visual SLAM methods with a focus on their applicability to embedded platforms. We analyze three main types of architectures - Geometric SLAM, Neural Radiance Fields (NeRF), and 3D Gaussian Splatting - and evaluate their performance on constrained hardware, specifically the NVIDIA Jetson AGX Orin. We compare their accuracy, segmentation quality, memory usage, and energy consumption. Our results show that methods based on NeRF and Gaussian Splatting achieve high semantic detail but demand substantial computing resources, limiting their use on embedded devices. In contrast, Semantic Geometric SLAM offers a more practical balance between computational cost and accuracy. The review highlights a need for SLAM algorithms that are better adapted to embedded environments, and it discusses key directions for improving their efficiency through algorithm-hardware co-design.
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