Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery

11/16/2022
by   Hao Qu, et al.
0

Self-supervised learning of egomotion and depth has recently attracted great attentions. These learning models can provide pose and depth maps to support navigation and perception task for autonomous driving and robots, while they do not require high-precision ground-truth labels to train the networks. However, monocular vision based methods suffer from pose scale-ambiguity problem, so that can not generate physical meaningful trajectory, and thus their applications are limited in real-world. We propose a novel self-learning deep neural network framework that can learn to estimate egomotion and depths with absolute metric scale from monocular images. Coarse depth scale is recovered via comparing point cloud data against a pretrained model that ensures the consistency of photometric loss. The scale-ambiguity problem is solved by introducing a novel two-stages coarse-to-fine scale recovery strategy that jointly refines coarse poses and depths. Our model successfully produces pose and depth estimates in global scale-metric, even in low-light condition, i.e. driving at night. The evaluation on the public datasets demonstrates that our model outperforms both representative traditional and learning based VOs and VIOs, e.g. VINS-mono, ORB-SLAM, SC-Learner, and UnVIO.

READ FULL TEXT

page 1

page 2

page 6

page 7

page 9

page 10

research
04/18/2023

Pose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion

Self-supervised monocular depth estimation approaches suffer not only fr...
research
01/31/2023

Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attacks

Monocular Depth Estimation (MDE) is a critical component in applications...
research
09/08/2020

Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation

The self-supervised loss formulation for jointly training depth and egom...
research
04/12/2020

Toward Hierarchical Self-Supervised Monocular Absolute Depth Estimation for Autonomous Driving Applications

In recent years, self-supervised methods for monocular depth estimation ...
research
09/16/2020

Calibrating Self-supervised Monocular Depth Estimation

In the recent years, many methods demonstrated the ability of neural net...
research
11/01/2020

Unsupervised Metric Relocalization Using Transform Consistency Loss

Training networks to perform metric relocalization traditionally require...
research
03/07/2022

Monocular Robot Navigation with Self-Supervised Pretrained Vision Transformers

In this work, we consider the problem of learning a perception model for...

Please sign up or login with your details

Forgot password? Click here to reset