Place recognition is a well defined but extremely challenging problem to solve in the general sense; given sensory information about a place such as a photo, can a human, animal, robot or personal navigation aid decide whether that place is the same as any places it has previously visited or learnt, despite the vast range of ways in which the appearance of that place can change.
Current approaches to the problem using GPS, cameras or lasers have one or more significant theoretical, technological or application-based limitations including high cost, sensitivity to changing environmental conditions, lack of generality, training requirements and long recognition latencies.
Developing a clear and concise definition of the place recognition problem.
Why focus on place recognition and not just object recognition, face recognition or SLAM?
State of the art research on how animals and humans perform place recognition.
Including state of the art research from fields like robotics and computer vision.
A categorized set of benchmark datasets that isolate various aspects of the place recognition problem including types of environmental change and pose invariance.
Applying game mechanics and game design techniques to motivate people to participate in the Place Recognition simulation.
Average Human Score
Average Machine Score
People who have beaten the Machine