3D Point Cloud Data Acquisition Using a Synchronized InAir Imaging Sonar Sensor Network Obtaining accurate data about the environment in which a robot is located is a crucial matter when it comes to autonomous navigation and other robotic applications. A popular method of acquiring this information is to use sonarrings, where a robot is fitted with multiple simple ultrasound transducers pointed in the directions where an object can appear. However, in a time where accurate 3D data is gaining importance, other sensing modalities are becoming more popular because of the ability to measure dense 3D point clouds. In these point clouds not only the horizontal plane is measured, but objects in the elevation planes can also be registered, which can be very interesting and makes applications such as 3D SLAM or object recognition possible. In this paper we present a way to extract complex 3D point cloud data from the entire surrounding sphere using multiple interconnected eRTIS sensors. These advanced imaging sonar sensors offer the flexibility of the popular sonarring in combination with the benefits of some of the competing sensing modalities. The setup presented here uses less sonar sensors (and thus less external hardware) while obtaining more information from the complete frontal hemispheres of each individual sensor. This setup is discussed, along with the issues that arise when using complex imaging sonar sensors in a network, and is tested in an indoor and outdoor environment. At the end of this paper is a discussion of the obtained results.

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.