# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Vision-Based Semantic Navigation Systems
#### 2.2.1 RGB Based Systems
[53] introduced a novel approach for indoor robotic applications, where they proposed the use of an occupancy grid map as a graph-based semantic mapping solution instead of relying on pre-existing maps. This approach adds robotic-specific, semantic, topological, and geometrical information to the OSM map. These maps have substantially less point-cloud data and rely on manually drawn maps with precise semantic elements (such as edges, corners, etc.) for localization and navigation. They provided models for fundamental building blocks that are semantically grouped into a network, such as walls, doors, and hallways. Then, it uses its hierarchical structure to enable precise navigation that is compatible with grid-based motion planning techniques. By incorporating the capability to query specific aspects, the models possess the capacity to identify task-relevant features within the immediate environment, thereby improving the performance of the robot planner. Three-way decisions (3 WD) and federated learning (FL) were applied in the research work [54] to improve mobile robot navigation (MRN) for Industry 5.0. The significance of MRN in promoting human-machine collaboration is emphasized. Authors proposed the employment of fuzzy sets (PFSs) to successfully handle information discrepancies and proposed the integration of FL to enable knowledge consolidation from various robots. Adjustable multi-granularity (MG) picture fuzzy (PF) probabilistic rough sets (PRSs) have been employed to construct an MRN technique based on FL and 3 WD. To ascertain the ideal granularity of MG PF membership degrees, the CODAS technique has been utilized. A case study was carried out to verify the viability of their approach utilizing MRN data from the Kaggle database. A solution to the problem of place recognition using point-cloud data was presented by [PERSON] et al. [55]. A brand new network architecture dubbed SOE-Net has been employed, which used orientation encoding and self-attention to record point relationships and integrate distant context into local descriptors. Authors presented a self-attention unit to capture feature dependencies and a PointOE module to capture local information from various orientations. Moreover, authors proposed a novel loss function, the HPHN quadruplet loss, which performed better than the current metric learning losses. Experimental results on benchmark datasets showed that SOE-Net outperformed state-of-the-art techniques in terms of performance. On the Oxford RobotCar dataset, the suggested network attained a recall of 89.37% at top 1 retrieval. The authors of [56] addressed the challenge of processing pixel-wise semantic segmentation in mobile robotic systems with limited computing resources. A light and efficient deep neural network architecture was proposed that can run in real-time on an embedded platform. The proposed model has the ability to segment navigable space in an image sequence, providing contextual awareness for autonomous vehicles based on machine vision. The study demonstrated the performance efficiency of the proposed architecture compared to existing models. The authors of [57] presented MiniNet-v2, an enhanced version of MiniNet, which is a model that assists in semantic segmentation for robotic applications with comparable accuracy to state-of-the-art models while using less memory and computational resources. The proposed architecture was validated through comprehensive experiments on public benchmarks, demonstrating its benefits over prior work. The work presented in [58] presented a method for performing semantic segmentation in indoor environments to aid robot navigation. The authors utilized deep-learning techniques to classify and segment various objects in real-time. The developed system was evaluated using a dataset of indoor images and demonstrated its effectiveness in accurately segmenting objects such as walls, floors, furniture, and doors. The authors of [59] proposed an end-to-end vision-based autonomous navigation framework called Agronav for agricultural robots and vehicles.