Robots that are expected to navigate efficiently through real-world environments need maps to localize themself and plan actions. These maps just contain purely geometric information as the robot needs a semantic understanding of its surroundings to fulfill their tasks. In this project, we address the problem of object-based mapping for mobile robots in indoor environments. The goal of the project is to realize a mapping system where an occupancy representation of the scene is enhanced with object level semantics. This high-level understanding of the scene should be suitable for other robots to localize and plan actions.

A gazebo simulation of an indoor environment is used. Through this environment we move a robot which collects RGBD and Lidar data.