  PointGoal navigation in indoor environment is a fundamental task for personal
robots to navigate to a specified point. Recent studies solved this PointGoal
navigation task with near-perfect success rate in photo-realistically simulated
environments, under the assumptions with noiseless actuation and most
importantly, perfect localization with GPS and compass sensors. However,
accurate GPS signalis difficult to be obtained in real indoor environment. To
improve the PointGoal navigation accuracy without GPS signal, we use visual
odometry (VO) and propose a novel action integration module (AIM) trained in
unsupervised manner. Sepecifically, unsupervised VO computes the relative pose
of the agent from the re-projection error of two adjacent frames, and then
replaces the accurate GPS signal with the path integration. The pseudo position
estimated by VO is used to train action integration which assists agent to
update their internal perception of location and helps improve the success rate
of navigation. The training and inference process only use RGB, depth,
collision as well as self-action information. The experiments show that the
proposed system achieves satisfactory results and outperforms the partially
supervised learning algorithms on the popular Gibson dataset.
