Visual Semantics Enable High-Performance Place Recognition From Opposing Viewpoints

"We wanted to replicate the process used by humans. Visual semantics works by not just sensing, but understanding where key objects are in the environment, and this allows for greater predictability in the actions that follow," Professor Milford said.
In what is believed to be a world first, Ph.D. student Sourav Garg, Dr. Niko Suenderhauf and Professor Michael Milford from QUT's Science and Engineering Faculty and Australian Centre for Robotic Vision, have used visual semantics to enable high-performance place recognition from opposing viewpoints.