Active simultaneous localization and mapping (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …

Abstract Autonomous Underwater Vehicles (AUVs) have been focused on by research
efforts because of their extensive applications in scientific, commercial as well as military …

In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the
perception and motion forecasting performance of self-driving vehicles. By intelligently …

We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

Underwater scene is highly unstructured, full of various noise interferences. Moreover, GPS
information is not available in the underwater environment, which thus brings huge …

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and
pose a long-term economic and environmental threat. Autonomous underwater vehicles …

Localisation, ie estimation of one's position in a given environment is a crucial element of
many mobile systems, manned and unmanned. Due to the high demand for autonomous …

Active learning is a decision-making process. In both abstract and physical settings, active
learning demands both analysis and action. This is a review of active learning in robotics …

Visual-inertial simultaneous localization and mapping (VI-SLAM) is popular research topic in
robotics. Because of its advantages in terms of robustness, VI-SLAM enjoys wide …

Even though the robotic machining has achieved great success in machining of small
components, it lacks the competence to machine large complex components, such as wind …