5 accepted papers
Understanding the environment is crucial for autonomous robots to perform navigation and manipulation tasks. Never-seen-before objects may have complex appearances and dynamics, where only physical interactions can help to identify visually hidden properties like mass or friction. In this work we pr
This paper introduces an Online Localisation and Colored Mesh Reconstruction (OLCMR) ROS perception architecture for ground exploration robots aiming to perform robust Simultaneous Localisation And Mapping (SLAM) in challenging unknown environments and provide an associated colored 3D mesh represent…
Many applications of Visual SLAM, such as augmented reality, virtual reality, robotics or autonomous driving, require versatile, robust and precise solutions, most often with real-time capability. In this work, we describe OV <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://ww
This article introduces a decentralized multi-robot algorithm for Simultaneous Localization And Mapping (SLAM) inspired from previous work on collaborative mapping [1]. This method makes robots jointly build and exchange i) a collection of 3D dense locally consistent submaps, based on a Truncated Si…
In this paper, we propose a novel cluster-based Next-Best-View path planning algorithm to simultaneously explore and inspect large-scale unknown environments with multiple Unmanned Aerial Vehicles (UAVs). In the majority of existing informative path-planning methods, a volumetric criterion is used f…