Event:

The brain circuitry involved in perceiving and encoding the world has been extensively tested over the past forty years, with an ever increasing body of knowledge about the components and wiring involved in navigation and perception tasks. The learning and recall of spatial features is known to take place in and around the hippocampus of the rodent, where there is clear evidence of cells that encode the rodent’s position and heading. RatSLAM is a primarily vision-based robotic navigation system based on current models of the rodent hippocampus, which has achieved several significant outcomes in vision-based Simultaneous Localization And Mapping (SLAM), including mapping of an entire suburb using only a low cost webcam, and navigation continuously over a period of two weeks in a delivery robot experiment. This research led to recent experiments demonstrating that impressive feats of vision-based navigation can be achieved at any time of day or night, during any weather, and in any season using visual images as small as 2 pixels in size.

In our current research we are investigating the problem of place recognition, visual navigation and general perception from two angles. The first is from a neuroscience-inspired perspective, modelling the multi-scale neuronal map of space found in the mammalian brain and the variably tolerant and selective visual recognition process in the primate and human brain. The second is from an algorithmic perspective, utilizing state of the art deep learning techniques. Together, these approaches are being applied in a wide range of domains, including navigation systems for underground mining vehicles, environmental and infrastructure monitoring and for visual servoing of sensor arms on planetary rovers. I will discuss the insights from this research, as well as current and future areas of study with the aim of stimulating discussion and collaboration.