This paper is an overview to Visual Simultaneous Localization and Map** (V-SLAM). We
discuss the basic definitions in the SLAM and vision system fields and provide a review of …

Visual SLAM (VSLAM) has been develo** rapidly due to its advantages of low-cost
sensors, the easy fusion of other sensors, and richer environmental information. Traditional …

We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM
consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Depth estimation is a classic task in computer vision, which is of great significance for many
applications such as augmented reality, target tracking and autonomous driving. Traditional …

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …

We propose D3VO as a novel framework for monocular visual odometry that exploits deep
networks on three levels--deep depth, pose and uncertainty estimation. We first propose a …

In recent years, Simultaneous Localization and Map** (SLAM) systems have shown
significant performance, accuracy, and efficiency gain. In this regard, Visual Simultaneous …

Over the last decade, the use of autonomous drone systems for surveying, search and
rescue, or last-mile delivery has increased exponentially. With the rise of these applications …

Estimating the 6D pose of objects from images is an important problem in various
applications such as robot manipulation and virtual reality. While direct regression of images …