Flexible Trinocular: Nonrigid MultiCameraIMU Dense Reconstruction for UAV Navigation and Mapping In this paper, we propose a visualinertial framework able to efficiently estimate the camera poses of a nonrigid trinocular baseline for longrange depth estimation onboard a fast moving aerial platform. The estimation of the timevarying baseline is based on relative inertial measurements, a photometric relative pose optimizer, and a probabilistic wing model fused in an efficient Extended Kalman Filter (EKF) formulation. The estimated depth measurements can be integrated into a georeferenced global map to render a reconstruction of the environment useful for local replanning algorithms. Based on extensive realworld experiments we describe the challenges and solutions for obtaining the probabilistic wing model, reliable relative inertial measurements, and visionbased relative pose updates and demonstrate the computational efficiency and robustness of the overall system under challenging conditions.

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.