Development of autonomous robotic platforms for indoor and outdoor mapping
Project Leader: Martin Tomko
Staff: Martin Tomko, Kourosh Khoshelham
Primary Contact: Martin Tomko (firstname.lastname@example.org)
Keywords: autonomous systems; data structures; robotics; spatial computing; spatial information
Disciplines: Infrastructure Engineering
The constant change of our outdoor and indoor environments require frequent updates to detailed maps. In this project, we will investigate the components of autonomous (re)mapping systems, such as self-driving cars, with a particular focus on:
- High definition map data storage and data structures, efficient partial updating and representation of (volumetric) data (such as data sensed by LIDAR), and the automated derivation of semantic information about these data (such as estimates of their temporary nature).
- Algorithms and techniques for efficient mapping and navigation through environments, including coverage path planning algorithms, environment decomposition and map integration by a robotic platform.
Suitable students will have an interested in spatial data structures and databases, machine learning and computer vision, and/or algorithmisation of spatial tasks. They will get a hands-on exposure to programming a robotic platform based on the ROS operating system. A knowledge of Python or C++ is a must.