语义SLAM是否已为嵌入式系统做好准备？一项比较性综述
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey
AI总结：
本文从嵌入式部署视角比较了基于几何、NeRF和高斯泼溅的语义SLAM系统，发现语义几何SLAM在精度与效率间最平衡，而高斯泼溅系统资源消耗大，并指出了嵌入式适配的研究空白与未来方向。
摘要：
语义SLAM有望在复杂环境中实现稳健的机器人导航，但其在嵌入式系统上的实用性仍不确定。本文从嵌入式部署的角度对现有语义视觉SLAM系统进行了比较性综述。与近期以隐式表示、NeRF或3D高斯泼溅作为场景表示的综述不同，我们的关注点是完整语义SLAM流水线在资源受限的机器人平台上的可部署性，特别强调NVIDIA Jetson AGX Orin。三种语义感知架构方法，即基于几何SLAM、NeRF和高斯泼溅的SLAM，分别从精度、语义重建质量、内存占用、功耗和吞吐量方面进行了评估。我们的Jetson测量结果表明，语义几何SLAM目前提供了精度与效率之间最实用的平衡，而基于高斯泼溅的系统需要超过16 GB的内存，功耗超过15 W，且远未达到实时执行。结果还表明，离线语义预处理可能掩盖语义集成的真实成本。因此，我们指出了在嵌入式系统上适配语义SLAM算法的研究空白，并提出了未来工作的方向，包括硬件-算法协同设计和能效优化。
英文摘要：
Semantic SLAM holds promise for robust robot navigation in complex environments, but its practicality on embedded systems remains uncertain. This paper presents a comparative survey of existing semantic visual SLAM systems from an embedded-deployment perspective. Unlike recent surveys centered on implicit representations, NeRFs, or 3D Gaussian Splatting as scene representations, our scope is the deployability of complete Semantic SLAM pipelines on resource-constrained robotics platforms, with particular emphasis on the NVIDIA Jetson AGX Orin. Three semantic-aware architectural approaches, namely Geometric SLAM, NeRF, and Gaussian Splatting-based SLAM, are evaluated with respect to accuracy, semantic reconstruction quality, memory footprint, power consumption, and throughput. Our Jetson measurements show that Semantic Geometric SLAM currently offers the most practical balance between accuracy and efficiency, whereas Gaussian Splatting-based systems require more than 16 GB of memory, draw more than 15 W, and remain far from real-time execution. The results also show that offline semantic preprocessing can mask the true cost of semantic integration. We therefore identify a research gap in adapting Semantic SLAM algorithms for embedded systems and propose directions for future work, including hardware-algorithm co-design and energy-efficient optimization.
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