Autonomous navigation of agricultural robots and vehicles in agricultural environments is a
prerequisite for the accomplishment of various tasks. However, precision navigation of …

Depth information is important for autonomous systems to perceive environments and
estimate their own state. Traditional depth estimation methods, like structure from motion …

In current decades, significant advancements in robotics engineering and autonomous
vehicles have improved the requirement for precise depth measurements. Depth estimation …

Deep learning based localization and mapping has recently attracted significant attention.
Instead of creating hand-designed algorithms through exploitation of physical models or …

Unsupervised methods have showed promising results on monocular depth estimation.
However, the training data must be captured in scenes without moving objects. To push the …

Monocular depth estimation is known as an ill-posed task that objects in a 2D image usually
do not contain sufficient information to predict their depth. Thus, it acts differently from other …

We propose XVO, a semi-supervised learning method for training generalized monocular
Visual Odometry (VO) models with robust off-the-self operation across diverse datasets and …

Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

Self-supervised VO methods have shown great success in jointly estimating camera pose
and depth from videos. However, like most data-driven methods, existing VO networks suffer …

Depth estimation from a camera is an important task for 3D perception. Recently, without
using the labeled ground truth of depth map, a self-supervised deep learning network can …