# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### LiDAR Based Semantic Navigation Systems  
These systems utilize data obtained from LiDAR sensors, which are processed, trained, and tested. LiDAR-based approaches aim to classify the environment type based on the LiDAR data, often performing pattern recognition for specific objects or places without relying on complex sensing technologies like vision systems. However, LiDAR-based semantic navigation has limitations in distinguishing between objects or places within the area of interest.  
LiDAR technology has been employed in various semantic navigation robot systems. In [22], the authors developed a semantic mapping system to classify three different types of buildings: single-family, multiple-family, and non-residential buildings. Additionally, classification systems for forest environment characteristics were developed in [23; 24]. In [25], an efficient classification system was proposed to continuously identify a person in a complex environment using LiDAR sensor information. Furthermore, [26] presented a method for the semantic classification of LiDAR data to distinguish between non-movable, movable, and dynamic objects.  
The work presented in [27] focused on classifying four types of indoor environments, including doors, halls, corridors, and rooms, for the purpose of indoor semantic navigation. The developed system could distinguish between these different places and construct a map area with efficient classification accuracy for indoor spaces. However, the use of LiDAR sensors offers limited semantic information due to the nature of the sensed data.  
A novel method for place recognition based on point clouds that were obtained from LiDAR scans was proposed by [PERSON] et al. [28], where the authors presented CASSPR, which uses cross-attention transformers to integrate voxel- and point-based techniques. In order to extract and aggregate data at a lower resolution, CASSPR uses a sparse voxel branch, and to capture fine-grained local features, it uses a point-wise branch. Cross-attention links the two branches together so that they can share information and add to the point cloud's global descriptor. Numerous datasets and extensive trials show that CASSPR achieves state-of-the-art performance in point-cloud place recognition tasks, outperforming earlier approaches by a large margin.  
In [29], the authors employed a neural network named Vehicle Points Completion-Net (VPC-Net) to combine incomplete and sparse data from mobile laser scanning (MLS) systems to create complete and dense point clouds of cars. The network is made up of three modules: refiner, decoder, and encoder. Using a point feature improvement layer and a spatial transformer network, the encoder module extracts global features. The refiner module adds fine-grained information to the outputs while maintaining vehicle details, while the decoder module produces the first complete outputs. Using both synthetic and real-scan datasets, the proposed VPC-Net was validated and showed better results than the baseline techniques.  
The employment of LiDAR data frames for the purpose of classifying the area of interest in indoor semantic navigation scenarios offers a fast, reliable, and low-overhead process approach. However, LiDAR-based semantic navigation systems offer a limited number of recognized objects (classes); this is due to the nature of the obtained data frames by the LiDAR perception unit.