Robot Navigation in Unseen Environments using Coarse Maps

Metric occupancy maps are widely used in autonomous robot navigation systems. However, when a robot is deployed in an unseen environment, building an accurate metric map is time-consuming. Can an autonomous robot directly navigate in previously unseen environments using coarse maps? In this work, we propose the Coarse Map Navigator (CMN), a navigation framework that can perform robot navigation in unseen environments using different coarse maps. To do so, CMN addresses two challenges: (1) novel and realistic visual observations; (2) error and misalignment on coarse maps. To tackle novel visual observations in unseen environments, CMN learns a deep perception model that maps the visual input from various pixel spaces to the local occupancy grid space. To tackle the error and misalignment on coarse maps, CMN extends the Bayesian filter and maintains a belief directly on coarse maps using the predicted local occupancy grids as observations. Using the latest belief, CMN extracts a global heuristic vector that guides the planner to find a local navigation action. Empirical results demonstrate that CMN achieves high navigation success rates in unseen environments, significantly outperforming baselines, and is robust to different coarse maps.