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.

翻译：在嵌入式系统中，机器人必须高效感知并解析其环境，方能在现实条件下可靠运行。视觉语义SLAM（同步定位与建图）通过将语义信息融入地图来增强传统SLAM，从而支持更智能的决策。然而，在资源受限的硬件上实现此类系统需要在精度、计算效率与功耗之间进行权衡。本文对近期语义视觉SLAM方法进行了比较性评述，重点关注其在嵌入式平台上的适用性。我们分析了三种主要架构类型——几何SLAM、神经辐射场（NeRF）与三维高斯泼溅——并评估了它们在受限硬件（特别是NVIDIA Jetson AGX Orin）上的性能表现。我们比较了这些方法的定位精度、分割质量、内存占用与能耗。研究结果表明，基于NeRF与高斯泼溅的方法能实现高语义细节度，但需要大量计算资源，限制了其在嵌入式设备上的应用。相比之下，语义几何SLAM在计算成本与精度之间提供了更实用的平衡。本综述指出当前亟需开发更适应嵌入式环境的SLAM算法，并探讨了通过算法-硬件协同设计提升其效率的关键方向。