## 3 Robot Scene Understanding for Navigation Planning  
For effective navigation at local and global scales, robots build maps to represent their external environments and to assist with making safe and accurate decisions at local levels. SLAM refers to the real-time combination of robot localisation and external environment mapping. SLAM is highly focused on mapping techniques. Early mobile ground robot map-building approaches comprised feature-based and photometric error-based methods. Robots could efficiently localise their positions in sparse or dense maps by implementing these mapping techniques. However, autonomous robots require more than basic 2D or 3D maps for effective task planning. Therefore, semantic SLAM techniques have been developed and investigated. Topological maps represent the environment as abstract graphs in contrast to classical metric maps. Classical metric maps are used in robotics to visualise the environment as a two-dimensional grid of cells, and each of these cells represents a specific location or area. The occupancy of a cell is typically represented by a binary value. However, these maps have limitations, such as their assumption of a flat and static environment and their inability to illustrate sensor data uncertainty. To overcome these limitations, researchers have developed probabilistic mapping techniques, such as occupancy grid mapping, which use probabilities to represent occupancy and can handle more complex and dynamic environments. The metric maps include details such as visited locations by a robot, proximity to features and general arrangement of external environments. One of the challenges in classical 3D geometric mapping is the accumulation of position errors over time at the global map level. Topological maps are not directly affected by these error-driven shifts in the geometrical maps, which means that topological mapping is more adaptable to different environmental contexts than geometric mapping. Classical and topological mapping methods have their strengths and weaknesses. Hybrid methods combine metric and topological mapping techniques to leverage advantages of each method. Many hybrid representations have been utilised by indoor robots to generate environmental mappings. Hybrid mapping representations from recent research works generally consist of two or more layers in a hierarchy (e.g., places, categories, ontology). The research work presented in [103] uses depth measurements, semantic segmentation and scene flow to map a 3D environment. This concept can detect the dynamic behaviour of objects. Neural implicit scalable encoding for SLAM [104] introduces a SLAM framework that utilises a hierarchical feature representation. It uses a hierarchical feature grid with encoder-decoder networks to predict occupancies. The geometric maps of indoor environments are developed by using extracted coarse, mid, and fine-level feature grids. The middle and fine-level feature grids represent observed scene geometry. The feature grid in the coarse level detects indoor geometries such as the floor and walls. The coarse level occupancy is used in the scene reconstruction of unobserved environment regions. In the reconstruction process, coarse-level occupancy is optimised in the mid-level feature grid and refined at the fine level. [PERSON] [105] presents a dynamic scene graph that consults a five-layer hierarchical representation. The method combines a metric semantic mesh with a topological spatial representation. It is capable of segmenting indoor structures and is robust in overcrowded environments. The five layers of the graph include metric-semantic mesh, objects and agents, places and structures, rooms, and buildings. To effectively implement these hybrid hierarchical maps in real-world applications, the representations need to be updated as the environment changes and should accurately map the interconnections between environment attributes.