Cooperative Rangeonly SLAM based on Sum of Gaussian Filter in Dynamic Environments Most rangeonly simultaneous localization and mapping (ROSLAM) algorithms have considered direct measurements between robot and its neighbor nodes only, even though other internode measurements can be actively used to improve the performance. In addition, most of them assume nodes are static, i.e., fixed on one place and cannot be applied to dynamic environments where some or all of nodes are moving. To address the aforementioned issues, a cooperative dynamic ROSLAM (CDROSLAM) is proposed in this paper, which is a ROSLAM algorithm based on sum of Gaussian (SoG) filter with cooperative measurements in dynamic environments. The proposed CDROSLAM integrates all the internode measurements for localization, which results in a smaller map estimation error with a faster convergence speed than the conventional ROSLAM. Moreover, the proposed CDROSLAM can also track movement of any nodes under the dynamic environment by resetting and updating the SoG variables. Such advantages of the proposed CDROSLAM algorithm are verified with the real experimental data.

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.