Objectbased differential Localization of Mobile Robots using sparse 2D Lidar Data The highly accurate pose estimation of mobile robots with respect to a known target object is a key technology for autonomous industrial manufacturing processes. Current approaches generally assume that the environment is static and locate the robot in relation to predefined positions. This paper presents an implementation and validation of how to localize a mobile robot in relation to the coordinate system of the target object  a prerequisite for any kind of manipulation or interaction. This allows the robot to be localized to arbitrarily positioned objects in the environment. For experimental validation, a highprecision external tracking system is used as ground truth. In this way, objects of different shapes are evaluated from different viewpoints. We achieve a pose estimation accuracy of less than 1 cm in a real world scenario.

MultiSensor 6DoF Localization For Aerial Robots In Complex GNSSDenied Environments The need for robots autonomously navigating in more and more complex environments has motivated intense Ru0026 D efforts in making robot pose estimation more accurate and reliable. This paper presents a multisensor multihypothesis method for robust 6DoF localization in complex environments. Robustness and accuracy requirements are addressed as follows. First, camera and LIDAR features are seamlessly integrated in the same statistical framework, benefiting from their synergies and providing robustness in scenarios with low or varying densities of LIDAR and visual features. Second, a multihypothesis approach is adopted to cope with scenario symmetries. The method has been carefully designed to operate in real time using feature and hypothesis filtering and efficient hypothesis refinement, and has been coded in a multicore implementation. The proposed method has been extensively validated for closedloop aerial robot navigation in different urban and industrial scenarios and has shown advantages over wellknown singlesensor techniques.