  Object goal navigation is an important problem in Embodied AI that involves
guiding the agent to navigate to an instance of the object category in an
unknown environment -- typically an indoor scene. Unfortunately, current
state-of-the-art methods for this problem rely heavily on data-driven
approaches, \eg, end-to-end reinforcement learning, imitation learning, and
others. Moreover, such methods are typically costly to train and difficult to
debug, leading to a lack of transferability and explainability. Inspired by
recent successes in combining classical and learning methods, we present a
modular and training-free solution, which embraces more classic approaches, to
tackle the object goal navigation problem. Our method builds a structured scene
representation based on the classic visual simultaneous localization and
mapping (V-SLAM) framework. We then inject semantics into geometric-based
frontier exploration to reason about promising areas to search for a goal
object. Our structured scene representation comprises a 2D occupancy map,
semantic point cloud, and spatial scene graph.
  Our method propagates semantics on the scene graphs based on language priors
and scene statistics to introduce semantic knowledge to the geometric
frontiers. With injected semantic priors, the agent can reason about the most
promising frontier to explore. The proposed pipeline shows strong experimental
performance for object goal navigation on the Gibson benchmark dataset,
outperforming the previous state-of-the-art. We also perform comprehensive
ablation studies to identify the current bottleneck in the object navigation
task.
