Бібліографічний опис:
Partsey, Ruslan. Robust Visual Odometry for Realistic PointGoal Navigation / Ruslan Partsey; Supervisor: Oleksandr Maksymets; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 87 p.: ill.
Короткий опис (реферат):
The ability to navigate in complex environments is a fundamental skill of a home
robot. Despite extensive study, indoor navigation in unseen environments under
noisy actuation and sensing and without access to precise localization continues
to be an open frontier for research in Embodied AI. In this work, we focus on designing
a visual odometry module for robust egomotion estimation and it’s integration
with navigation policy for efficient navigation under noisy actuation and sensing.
Specifically, we study how the observations transformations and incorporating
meta-information available to the navigation agent impacts visual odometry model
generalization performance. We present a set of regularization techniques that can
be implemented as train- and test-time augmentations to increase the robustness to
noise.
Navigation agent, equipped with our visual odometry module, reaches the goal
in 86% of episodes and scores 0.66 SPL in Habitat Challenge 2021 benchmark.
