Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Autonomous driving vehicles require both a precise localization and map** solution in
different driving environment. In this context, Simultaneous Localization and Map** …

Dense simultaneous localization and map** (SLAM) is crucial for robotics and augmented
reality applications. However current methods are often hampered by the non-volumetric or …

This paper addresses the problem of estimating the depth map of a scene given a single
RGB image. We propose a fully convolutional architecture, encompassing residual learning …

We propose a semi-direct monocular visual odometry algorithm that is precise, robust, and
faster than current state-of-the-art methods. The semi-direct approach eliminates the need of …

Stereo matching estimates the disparity between a rectified image pair, which is of great
importance to depth sensing, autonomous driving, and other related tasks. Previous works …

SLAM is an abbreviation for simultaneous localization and map**, which is a technique for
estimating sensor motion and reconstructing structure in an unknown environment …

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit
information from all intensity gradients in the image. However, low computational speed as …

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly
using pixel intensity errors of image patches, achieves accurate tracking performance while …

The ability to predict depth from a single image-using recent advances in CNNs-is of
increasing interest to the vision community. Unsupervised strategies to learning are …