# 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
The framework utilizes semantic segmentation and semantic line detection models to accurately identify key components in the scene and detect boundary lines that separate the crops from the traversable ground. Unlike existing methods that rely on expensive equipment, like RTK-GNSS and LiDAR, Agronav is entirely vision-based, requiring only a single RGB camera. Scene recognition is the main target of the algorithms proposed by the authors of [60]. The proposed algorithms are real-time algorithms for segmenting and detecting contrast objects in color images, aiming to provide stable segmentation components. The proposed techniques were based on the geometrized histogram method and were applied to analyze video sequences and identify visual landmarks for autonomous robot navigation. The proposed approach combines contour-based and region-based approaches and offers a vector description of contrast objects, enabling the fast evaluation of their shape and classification. Authors revealed that the proposed technique is especially useful for navigation in unknown environments, and it was applied to solve navigation problems for mobile robots in indoor environments. [PERSON] et al. [61] proposed a framework for navigation in dynamic environments using semantic maps generated through deep-learning object segmentation. The challenge of using geometry-free representations for navigation in home environments has been addressed, where the environment can undergo dynamic changes. The developed system consists of a two-step approach: first, generating a deep-learning-enabled semantic map from annotated world representations, and second, performing object-based navigation using the learned semantic map representation. The developed framework allows robots to autonomously generate a dataset for deep learning and transfer existing geometric map-based task execution systems to a semantic map-based one that is invariant to changes in object location. A real-time solution for extracting corridor scenes from a single image using a lightweight semantic segmentation model was proposed in [62]. The model combines an FCN decoder and MobilenetV2 encoder, allowing for high precision with minimized computation time. The study also introduced the use of the balance cross-entropy loss function to handle diverse datasets and incorporated various techniques, such as the Adam optimizer and Gaussian filters, to enhance segmentation performance. Experimental results demonstrated that the proposed model outperformed baseline models across different datasets and maintain consistent performance when applied to a real mobile robot, supporting optimal path planning and efficient obstacle avoidance. The adoption of RGB-based methods with robot semantic navigation systems may enhance the navigation capability and reliability. In addition, RGB-based vision systems offer semantic information about the navigation environment, which are better than range-finder systems. However, the processing of RGB images is not a simple task in several scenarios, as the RGB images suffer from several problems, including low light and occlusion. In addition, the adoption of RGB-based vision systems lacks the geometry information obtained by the LiDAR-based systems.