IntentionNet: Map-Lite Visual Navigation at the Kilometre Scale

Inspired by modern datadriven through diverse environments to distant goals? This remains approaches, the lower level of our system design is an open challenge due to the complexity and difficulty of a neural network-based controller that maps observations designing a robot that can generalise over environments, directly to velocity commands, and which is learned end-toend tolerate significant mapping and positioning inaccuracies from real world experience. Neural networks have the and recover from inevitable navigation errors. While many flexibility to accept a wide variety of input types, and we works tackle robot navigation, few systems capable of find that design space for the signals used by the system's long-range, kilometre-scale navigation exist. Classical robot upper level to guide the lower level is large. We exploit systems capable of long-range navigation like Montemerlo this property to design several different types of guidance et al. (2008); Kümmerle et al. (2013) use e xplicit signals, which we call intentions. We find that designing maps and find paths over them using classical planning the appropriate intention imbues the navigation system with algorithms (Siegwart et al. 2011), allowing them to reach specific abilities, such as the ability to tolerate significant arbitrarily distant goals in principle.