This article presents a comprehensive review of the Active Simultaneous Localization and
Mapping (A-SLAM) research conducted over the past decade. It explores the formulation …

Object SLAM is considered increasingly significant for robot high-level perception and
decision-making. Existing studies fall short in terms of data association, object …

Accurate perception of objects in the environment is important for improving the scene
understanding capability of SLAM systems. In robotic and augmented reality applications …

In semantic visual simultaneous localization and mapping (SLAM), accurate object-level
reconstruction of the environment based on the deep learning techniques is very crucial for …

Object SLAM uses additional semantic information to detect and map objects in the scene, in
order to improve the system's perception and map representation capabilities. Previous …

Robotic eye-in-hand calibration is the task of determining the rigid 6-DoF pose of the camera
with respect to the robot end-effector frame. In this paper, we formulate this task as a non …

6D pose estimation of textureless shiny objects has become an essential problem in many
robotic applications. Many pose estimators require high-quality depth data, often measured …

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the
complex-step finite difference (CSFD) method for efficient calculation of numerical …

Relocalization is a critical component of robotics applications, it poses challenges due to
changes in lighting conditions, weather, and viewing point. Image feature-based …

In the evolving landscape of modern robotics, Visual SLAM (V-SLAM) has emerged over the
past two decades as a powerful tool, empowering robots with the ability to navigate and map …