This paper develops a real-time decentralized metric-semantic Simultaneous Localization
and Mapping (SLAM) approach that leverages a sparse and lightweight object-based …

Dynamic SLAM is a key technology for autonomous driving and robotics, and accurate pose
estimation of surrounding objects is important for semantic perception tasks. Current quadric …

This letter introduces a novel method for object-level relocalization of robotic systems. It
determines the pose of a camera sensor by robustly associating the object detections in the …

With the continuous development of robotics and computer vision technology, mobile robots
have been widely applied in various fields. In this process, semantic maps for robots have …

This paper describes a multi-modal data association method for global localization using
object-based maps and camera images. In global localization, or relocalization, using object …

Tracking and modeling unknown rigid objects in the environment play a crucial role in
autonomous unmanned systems and virtual-real interactive applications. However, many …

In passive Internet of Things, existing synthetic aperture-based 3D localization methods face
many challenges, such as high computational load, a large aperture of a virtual antenna …

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 …

It is a fundamental measurement task to accurately estimate the 6D object pose for robotic
manipulations. Utilizing multi-view RGB observations to estimate poses is a reasonable …

Simultaneous localization and mapping (SLAM) is the process of estimating the trajectory of
a mobile sensor carrier and creating a representation of its surroundings. Traditional SLAM …