Autonomous navigation requires both a precise and robust map** and localization
solution. In this context, Simultaneous Localization and Map** (SLAM) is a very well …

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

Grounding language to the visual observations of a navigating agent can be performed
using off-the-shelf visual-language models pretrained on Internet-scale data (eg, image …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

We present vMAP, an object-level dense SLAM system using neural field representations.
Each object is represented by a small MLP, enabling efficient, watertight object modelling …

A key requirement for leveraging supervised deep learning methods is the availability of
large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very …

Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization
And Map** (SLAM) yet face challenges such as error accumulation during camera …

A key component of Simultaneous Localization and Map** (SLAM) systems is the joint
optimization of the estimated 3D map and camera trajectory. Bundle adjustment (BA) is the …

Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years,
leading to a vast number of applications, such as, terrain exploration, disaster assistance …

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 …