What makes a place? Building bespoke place dependent object detectors for robotics
Jeffrey Hawke, Alex Bewley, Ingmar Posner · 2017
This paper is about enabling robots to improve their perceptual performance through repeated use in their operating environment, creating local expert detectors fitted to the places through which a robot moves. We leverage the concept of `experiences' in visual perception for robotics, accounting for bias in the data a robot sees by fitting object detector models to a particular `place'. The key question we seek to answer in this paper is simply: how do we define a place? We build bespoke pedestrian detector models for autonomous driving, highlighting the necessary trade off between generalisation and model capacity as we vary the extent of the `place' we fit to. We demonstrate a sizeable performance gain over a current state-of-the-art detector when using computationally lightweight bespoke place-fitted detector models.