Design of an information fusion for semantic segmentation of LiDAR point clouds in an urban context.

Context

Platooning describes the automated driving of vehicles in a convoy, whereby the vehicles are networked with each other and observe the environment with the help of sensors. An important aspect in the process chain is the acquisition and processing of environmental data. LiDAR sensors generate three-dimensional point clouds of objects in the vehicle's environment. This data can be used, for example, to determine position or detect obstacles. An important step in the processing of LiDAR point cloud data is segmentation. This involves dividing the points in the point cloud into objects (for example, roads, buildings, vehicles) to enable better interpretation of the data.

In addition to LiDAR sensors, modern vehicles have other sensors such as RGB cameras, RADAR sensors or GPS sensors. These represent a source of information and can be considered by information fusion techniques for semantic segmentation. More information about the Tempus project can be found at https://tempus-muenchen.de.

Goals

• Overview of the state of the art of information fusion techniques in the context of semantic segmentation.
• Design of a semantic segmentation pipeline considering a sensor network
• Implementation of the designed concepts
• Evaluation of the designed concept based on simulation/real data

Requirements

• Interest in information fusion systems
• Sound programming skills (Python/C++)
• Reliable and independent way of working