Localisation under Large Appearance Change - Church, Matthew (Department of Electrical Engineering, 2024)
Localisation is a foundational building block for more complex robot applications, and thus if low-cost localisation solutions can be found, the number of activities a robot can undertake will increase. However, appearance-based localisation systems in the past have required frequent traversals of the environment in order to sufficiently capture the change indicative of that environment. There are applications such as agriculture in which this frequent data collection is not appropriate. This thesis presents an appearance-based localisation system that combines generated and recorded data in the form of experience-localiser pairs combined to create an experience based network that can be used for localisation. The inclusion of generated data reduces the requirement for frequent data collection, provided an adequate generation model can be trained. The experience, which is a collection of images and transforms describing a traversal of an environment is the primary means through which this generation of data can influence the network. The images contained in the generated experiences were created from two parent experiences capturing two specific times of the day. The network trained learns a mapping from the two parent experiences creating intermediate domains that represent times between the parents, effectively filling in the gaps left by sparse data collection. While the performance of the generation network narrows the functional scope of the system, within that narrow scope, experiences generated from recorded outings outperform the recorded counterparts provided the parent does as well, such that an experience generated from a recording collected at 10:00 and made to mimic the conditions at 14:00 will outperform the recording collected at 14:00. Should a version be used such that all recorded experiences are utilized as a collective, the system outperforms that of a system making use of just recorded data