Fit for Purpose? Predicting Perception Performance Based on Past Experience

Corina Gurau, Chi Hay Tong, Ingmar Posner

Abstract: This paper explores the idea of predicting the likely performance of a robot’s perception system based on past experience in the same workspace. In particular, we propose to build a place-specific model of perception performance from observations gathered over time. We evaluate our method in a classical decision making scenario in which the robot must choose when and where to drive autonomously in 60 km of driving data from an urban environment. We demonstrate that leveraging visual appearance within a state-of-the-art navigation framework increases the accuracy of our performance predictions.