# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 1 Introduction  
Traditional Simultaneous Localization and Mapping (SLAM) techniques predominantly generate maps with geometric information. However, their deficiency in environmental semantic comprehension restricts their extensive deployment in intricate scenarios. In recent times, propelled by the rapid advancements in deep learning technologies, semantic SLAM has emerged as a focal area of research. Semantic SLAM [1], an advanced technology integrating semantic understanding and map building, is designed to augment a robot's localization and navigation capabilities within uncharted and complex environments. By fusing deep learning techniques with SLAM, this approach enables the effective segmentation and precise recognition of environmental semantic information. As a result, it furnishes more comprehensive and accurate data for robot localization and navigation in complex settings. Semantic segmentation is a fundamental task in computer vision that aims to classify each pixel in an image into specific semantic categories, such as objects, scenes, or parts of objects. This technique bridges the gap between low-level image features and high-level semantic understanding, enabling machines to perceive the environment in a more human-like manner. In the context of this research, semantic segmentation plays a crucial role in the SLAM system. By integrating semantic segmentation with SLAM, our system can not only build geometric maps of the environment but also understand the semantic information of different objects and scenes. This semantic information is vital for complex tasks such as disaster rescue, where identifying obstacles, trapped individuals, and other critical elements accurately can significantly improve the efficiency and safety of rescue operations. In the critical domain of disaster rescue, the SLAM technology assumes a pivotal role. It empowers rescue personnel to expeditiously and precisely acquire information regarding the disaster site, thereby providing invaluable support for rescue operations [2]. Notably, this technology incorporates environmental semantic information into the map construction process. It achieves the accurate segmentation of crucial semantic elements such as obstacles in rescue corridors and trapped individuals, facilitating the generation of maps replete with rich semantic content. These maps not only delineate the physical space layout but also present rescue workers with a more intuitive and detailed perspective of the disaster scene. Consequently, the efficiency and safety of rescue operations are substantially enhanced. Rescue robots can execute precise navigation, effectively circumvent obstacles, and choose the optimal route to reach the disaster stricken area. Neural Radiance Fields (NeRF), a neural network-based implicit representation technology, has emerged as a revolutionary approach in the field of SLAM [3]. At its core, NeRF represents a 3D scene as a continuous volumetric field, where neural networks learn to map 3D coordinates to view-dependent radiance and density values. This enables the technology to capture intricate lighting and geometric details with high fidelity. In the context of SLAM, NeRF has demonstrated remarkable potential in achieving full-density optimization and the joint learning of geometry and semantics, thus providing more comprehensive environmental understanding [4]. Nevertheless, such methods are plagued by several drawbacks, including high computational costs, substantial resource consumption, real-time challenges, reliance on high-quality data, and limited pose estimation accuracy. These limitations impede their widespread application in scenarios with stringent real-time requirements, such as disaster rescue. Notably, the integration of NeRF and SLAM technologies can utilize per-frame image data to construct high-quality 3D scene models and achieve precise spatial perception, 3D reconstruction, and dynamic localization.