  In embodied vision, Instance ImageGoal Navigation (IIN) requires an agent to
locate a specific object depicted in a goal image within an unexplored
environment. The primary difficulty of IIN stems from the necessity of
recognizing the target object across varying viewpoints and rejecting potential
distractors.
  Existing map-based navigation methods largely adopt the representation form
of Bird's Eye View (BEV) maps, which, however, lack the representation of
detailed textures in a scene.
  To address the above issues, we propose a new Gaussian Splatting Navigation
(abbreviated as GaussNav) framework for IIN task, which constructs a novel map
representation based on 3D Gaussian Splatting (3DGS).
  The proposed framework enables the agent to not only memorize the geometry
and semantic information of the scene, but also retain the textural features of
objects.
  Our GaussNav framework demonstrates a significant leap in performance,
evidenced by an increase in Success weighted by Path Length (SPL) from 0.252 to
0.578 on the challenging Habitat-Matterport 3D (HM3D) dataset.
  Our code will be made publicly available.
