Adversarial Learning for Joint Optimization of Depth and Ego-Motion.

Area of Science:

• Computer Vision
• Deep Learning
• Robotics

Background:

• Supervised deep learning for dense depth estimation requires extensive high-quality data.
• Self-supervised methods face challenges with scale ambiguity and precise pose estimation.
• Existing approaches struggle to simultaneously achieve accurate depth and ego-motion estimation.

Purpose of the Study:

• To develop a joint self-supervised deep learning pipeline for accurate depth and ego-motion estimation.
• To address the limitations of current monocular and binocular depth estimation techniques.
• To improve the precision of ego-motion estimation in autonomous systems.

Main Methods:

• A joint self-supervised deep learning pipeline integrating adversarial learning and spatial-temporal geometrical constraints.
• Utilizing stereo reconstruction error for absolute scale depth estimation.
• Employing direct visual odometry (DVO) optimization with backpropagation for fine-grained ego-motion.
• Iterative coupling optimization with adversarial learning for enhanced accuracy.

Main Results:

• The proposed method achieves superior performance in monocular depth and ego-motion estimation compared to state-of-the-art approaches.
• Demonstrated strong generalization capabilities on the KITTI dataset.
• Successfully resolved scale ambiguity and improved pose change estimation.

Conclusions:

• The joint pipeline effectively estimates both depth and ego-motion accurately and precisely.
• The approach offers a robust and generalizable solution for self-supervised depth and motion estimation.
• This work advances the field of autonomous navigation and scene understanding.