外观
论文arXiv 2505.123842 个月前
语义 SLAM 准备好用于嵌入式系统了吗？一项比较综述
原文链接：Is semantic SLAM ready for embedded systems? A comparative survey（arXiv 2505.12384 · 发布日期：2026-08-29）
Is semantic SLAM ready for embedded systems? A comparative survey
本文从嵌入式部署视角比较语义视觉 SLAM 管线，在 Jetson AGX Orin 上评估三类方法的精度与效率。
核心要点
- 语义 SLAM 有望提升复杂环境导航鲁棒性，但在嵌入式系统上的实用性仍不确定。
- 综述聚焦资源受限机器人平台上的完整管线可部署性，而非单独的场景表示。
- 比较 Geometric SLAM、NeRF 与 Gaussian Splatting 类方法的精度、语义重建、内存、功耗和吞吐。
- Jetson 测量显示语义 Geometric SLAM 目前更平衡，离线语义预处理可能掩盖真实集成成本。
验证范围与局限：验证范围为现有语义视觉 SLAM 系统的比较综述，并重点基于 NVIDIA Jetson AGX Orin 测量。
英文摘要 (Abstract)
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.
作者：Calvin Galagain, Martyna Porêba, François Goulette
来源
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