Related papers: MmWave Mapping and SLAM for 5G and Beyond

Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress…

Achieving real-time Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian splatting (3DGS) in large-scale real-world environments remains challenging, as existing methods still struggle to jointly achieve low-latency pose…

The LiDAR and inertial sensors based localization and mapping are of great significance for Unmanned Ground Vehicle related applications. In this work, we have developed an improved LiDAR-inertial localization and mapping system for…

This paper uses the smoothing and mapping framework to solve the SLAM problem in indoor environments; focusing on how some key issues such as feature extraction and data association can be handled by applying probabilistic techniques. For…

We present a novel Simultaneous Localization and Mapping (SLAM) method that employs Gaussian Process (GP) based landmark (object) representations. Instead of conventional grid maps or point cloud registration, we model the environment on a…

This research explores the integration of indoor Simultaneous Localization and Mapping (SLAM) with Augmented Reality (AR) to enhance situational awareness, improving safety in hazardous or emergency situations. The main contribution of this…

Digital maps will revolutionize our experience of perceiving and navigating indoor environments. While today we rely only on the representation of the outdoors, the mapping of indoors is mainly a part of the traditional SLAM problem where…

The simultaneous localization and mapping (SLAM) problem is considered in three dimensions. The proposed algorithm, differential geometric SLAM (DG-SLAM), employs methods from differential geometry to propagate the state and map estimates.…

Cloud Robotics is one of the emerging area of robotics. It has created a lot of attention due to its direct practical implications on Robotics. In Cloud Robotics, the concept of cloud computing is used to offload computational extensive…

In dynamic scenes, both localization and mapping in visual SLAM face significant challenges. In recent years, numerous outstanding research works have proposed effective solutions for the localization problem. However, there has been a…

Simultaneous Localization and Mapping (SLAM) algorithms are frequently deployed to support a wide range of robotics applications, such as autonomous navigation in unknown environments, and scene mapping in virtual reality. Many of these…

This paper explores an integrated approach for improved sensing and positioning with applications in air traffic management (ATM) and in the Advanced Surface Movement Guidance and Control System (A-SMGCS). The integrated approach includes…

LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other…

Millimeter Waves (mmW) and sub-THz frequencies are the candidate bands for the upcoming Sixth Generation (6G) of communication systems. The use of collimated beams at mmW/sub-THz to compensate for the increased path and penetration loss…

Next-generation wireless networks will see the convergence of communication and sensing, also exploiting the availability of large bandwidths in the Terahertz (THz) spectrum and electrically large antenna arrays on handheld devices. In…

With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the community. Yet collaborating SLAM over multiple robots still remains…

SLAM technology plays a crucial role in indoor mapping and localization. A common challenge in indoor environments is the "double-sided mapping issue", where closely positioned walls, doors, and other surfaces are mistakenly identified as a…

Reliable and accurate localization and mapping are key components of most autonomous systems. Besides geometric information about the mapped environment, the semantics plays an important role to enable intelligent navigation behaviors. In…

Networks in 5G and beyond utilize millimeter wave (mmWave) radio signals, large bandwidths, and large antenna arrays, which bring opportunities in jointly localizing the user equipment and mapping the propagation environment, termed as…

Simultaneous Localization and Mapping (SLAM) algorithms perform visual-inertial estimation via filtering or batch optimization methods. Empirical evidence suggests that filtering algorithms are computationally faster, while optimization…