Semantic Understanding and Learning for Range-based Indoor Robots (2)
Motivation and scope
Mobile robots have the potential to revolutionise our daily lives and help with many everyday chores. The more a robot understands about the complex world around it, the better it can navigate through its environment, more efficiently using its resources, completing its tasks, and interacting with its users. An ongoing research problem is to help robots better understand the world by interpreting incoming sensor data into semantically meaningful information and using it to build models of the world.
This project will be performed in partnership with Electrolux. In 2017 Electrolux launched its robot vacuum cleaner, the PURE i9. The PURE i9 uses a 3D vision sensor, using an infra-red (IR) camera and 2 IR line lasers to build a 3D map of the environment. Electrolux has acquired a large supply of sensor data and maps from robots in their training environments which will be made available for algorithm development.
For indoor vacuum cleaning robots, it is useful to be able to identify and classify rooms in the environment – for example, a kitchen may need more frequent cleaning than other rooms. The goal of this project is to implement methods that automatically labels the robot’s map with information about the rooms in its environment. This process is known as room segmentation. If time allows, the project will also investigate how room classification information (whether a room is a kitchen, bedroom, etc) can also be learned.
This project will require an investigation of existing room segmentation and room classification techniques for range-based maps, assessing both supervised and unsupervised approaches. The project will perform evaluation on data acquired from Electrolux’s Simultaneous Localisation and Mapping (SLAM) system and determine how effectively important map elements can be identified using these machine learning algorithms.
This project offers the opportunity to work in partnership with engineers at Electrolux, one of the world’s largest appliance makers and a company that has identified robotic vacuum cleaners as a priority for their home care and small domestic appliances. You will gain hands-on experience with machine learning and robot mapping using real-world data.