As robots are increasingly integrated into large and dynamic environments alongside humans, there is a pressing need for efficient onboard solutions to fundamental robotic operations, such as navigation and decision-making. Existing solutions often rely on computationally intensive processes that do not scale well in larger environments, leading to long computation times. This can result in unsafe and non-adaptive behaviors, as during the planning phase the robot continues to move along an old and potentially dangerous path increasing the risks of accidents or emergency.This thesis addresses this challenge by developing human-inspired light-weight methods, that enhance robotic navigation and environment understanding.

The central framework presented in this thesis introduces a novel human-like method for navigation and environment segmentation using 2D grid maps, focusing on extracting structural-semantics, such as intersections, pathways, dead ends, and paths to unexplored areas. The framework also generates sparse topometric maps for lightweight robotic navigation by using structural-semantic information. Compared to the state-of-the-art, where map segmentation either utilizes features that are specific to some indoor environments or segments into arbitrary regions that do not convey semantically meaningful information about the environment, the semantic topometric map captures structural-semantic information, which can easily be utilized by robots in a variety of missions. The proposed framework has been validated on multiple maps of different sizes and types of environments. In comparison with the state-of-the-art topological maps generated by Voronoi-based solutions, the proposed framework shows a significant reduction in complexity and computation times required in solving navigation problems. 

The utility of structural semantics is demonstrated through a novel autonomous exploration strategy that integrates structural-semantic information with conventional metric data for goal/frontier selection and employs the semantic topometric map for navigating to a frontier. The effectiveness of the exploration strategy is demonstrated in real-world experiments, showcasing improved exploration speed and computational efficiency compared to frontier-based exploration methods using only metric information. 

In order to enable the methods presented in this thesis to operate over 3D maps, this thesis introduces an approach for converting 3D voxel maps into 2D occupancy maps augmented with height and slope information. Moreover, a method for converting paths generated in 2D into 3D paths is proposed. This allows for the use of structural-semantic segmentation and efficient topometric map-based navigation planning for both UAVs and UGVs. These contributions together enable lightweight and fast environment segmentation and navigation planning for a multitude of robot types, and leveraging structural-semantic information leads to a more human-like approach toward robotic navigation and environment understanding.