Miao Le, Liu Wen, Deng Zhongliang
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2025 Aug 12;25(16):4994. doi: 10.3390/s25164994.
With the growing demand for autonomous robotic operations in complex and unstructured environments, traditional semantic SLAM systems-which rely on closed-set semantic vocabularies-are increasingly limited in their ability to robustly perceive and understand diverse and dynamic scenes. This paper focuses on the paradigm shift toward open-world semantic scene understanding in SLAM and provides a comprehensive review of the technological evolution from closed-world assumptions to open-world frameworks. We survey the current state of research in open-world semantic SLAM, highlighting key challenges and frontiers. In particular, we conduct an in-depth analysis of three critical areas: zero-shot open-vocabulary understanding, dynamic semantic expansion, and multimodal semantic fusion. These capabilities are examined for their crucial roles in unknown class identification, incremental semantic updates, and multisensor perceptual integration. Our main contribution is presenting the first systematic algorithmic benchmarking and performance comparison of representative open-world semantic SLAM systems, revealing the potential of these core techniques to enhance semantic understanding in complex environments. Finally, we propose several promising directions for future research, including lightweight model deployment, real-time performance optimization, and collaborative multimodal perception, and offering a systematic reference and methodological guidance for continued advancements in this emerging field.
随着在复杂和非结构化环境中对自主机器人操作的需求不断增长，传统的语义同步定位与地图构建（SLAM）系统——依赖于封闭集语义词汇表——在强大地感知和理解多样且动态场景的能力方面越来越受限。本文聚焦于SLAM中向开放世界语义场景理解的范式转变，并全面回顾了从封闭世界假设到开放世界框架的技术演进。我们调研了开放世界语义SLAM的当前研究状况，突出关键挑战和前沿领域。特别地，我们对三个关键领域进行了深入分析：零样本开放词汇理解、动态语义扩展和多模态语义融合。考察了这些能力在未知类别识别、增量语义更新和多传感器感知整合中的关键作用。我们的主要贡献是给出了首个对代表性开放世界语义SLAM系统的系统算法基准测试和性能比较，揭示了这些核心技术在增强复杂环境中语义理解方面的潜力。最后，我们提出了几个未来研究的有前景方向，包括轻量级模型部署、实时性能优化和协作多模态感知，并为这一新兴领域的持续进步提供系统参考和方法指导。
