Unsupervised Simultaneous Learning for Camera Re-Localization and Depth Estimation from Video

TL;DR

This paper introduces an unsupervised learning framework that simultaneously estimates camera re-localization and depth from unlabeled video, outperforming existing methods in accuracy and robustness.

Contribution

It proposes a novel unsupervised approach for camera re-localization and depth estimation using only monocular videos, eliminating the need for labeled training data.

Findings

Outperforms state-of-the-art visual SLAM (ORB-SLAM3) in re-localization accuracy.

Achieves superior monocular depth estimation within trained environments.

Demonstrates effectiveness on the 7-scenes dataset.

Abstract

We present an unsupervised simultaneous learning framework for the task of monocular camera re-localization and depth estimation from unlabeled video sequences. Monocular camera re-localization refers to the task of estimating the absolute camera pose from an instance image in a known environment, which has been intensively studied for alternative localization in GPS-denied environments. In recent works, camera re-localization methods are trained via supervised learning from pairs of camera images and camera poses. In contrast to previous works, we propose a completely unsupervised learning framework for camera re-localization and depth estimation, requiring only monocular video sequences for training. In our framework, we train two networks that estimate the scene coordinates using directions and the depth map from each image which are then combined to estimate the camera pose. The networks can be trained through the minimization of loss functions based on our loop closed view synthesis. In experiments with the 7-scenes dataset, the proposed method outperformed the re-localization of the state-of-the-art visual SLAM, ORB-SLAM3. Our method also outperforms state-of-the-art monocular depth estimation in a trained environment.