  This work explores the challenges of creating a scalable and robust robot
navigation system that can traverse both indoor and outdoor environments to
reach distant goals. We propose a navigation system architecture called
IntentionNet that employs a monolithic neural network as the low-level
planner/controller, and uses a general interface that we call intentions to
steer the controller. The paper proposes two types of intentions, Local Path
and Environment (LPE) and Discretised Local Move (DLM), and shows that DLM is
robust to significant metric positioning and mapping errors. The paper also
presents Kilo-IntentionNet, an instance of the IntentionNet system using the
DLM intention that is deployed on a Boston Dynamics Spot robot, and which
successfully navigates through complex indoor and outdoor environments over
distances of up to a kilometre with only noisy odometry.
