Vision Based Position Control for MAVs Using One Single Circular Landmark

This paper presents a real-time vision based algorithm for 5 degrees-of-freedom pose estimation and set-point control for a Micro Aerial Vehicle (MAV). The camera is mounted on-board a quadrotor helicopter. Camera pose estimation is based on the appearance of two concentric circles which are used as landmark. We show that that by using a calibrated camera, conic sections, and the assumption that yaw is controlled independently, it is possible to determine the six degrees-of-freedom pose of the MAV. First we show how to detect the landmark in the image frame. Then we present a geometric approach for camera pose estimation from the elliptic appearance of a circle in perspective projection. Using this information we are able to determine the pose of the vehicle. Finally, given a set point in the image frame we are able to control the quadrotor such that the feature appears in the respective target position. The performance of the proposed method is presented through experimental results.     

Fusion of IMU and Vision for Absolute Scale Estimation in Monocular SLAM

The fusion of inertial and visual data is widely used to improve an object??s pose estimation. However, this type of fusion is rarely used to estimate further unknowns in the visual framework. In this paper we present and compare two different approaches to estimate the unknown scale parameter in a monocular SLAM framework. Directly linked to the scale is the estimation of the object??s absolute velocity and position in 3D. The first approach is a spline fitting task adapted from Jung and Taylor and the second is an extended Kalman filter. Both methods have been simulated offline on arbitrary camera paths to analyze their behavior and the quality of the resulting scale estimation. We then embedded an online multi rate extended Kalman filter in the Parallel Tracking and Mapping (PTAM) algorithm of Klein and Murray together with an inertial sensor. In this inertial/monocular SLAM framework, we show a real time, robust and fast converging scale estimation. Our approach does not depend on known patterns in the vision part nor a complex temporal synchronization between the visual and inertial sensor.     

A Laser Synthesis Route to Boron-Doped Gold Nanoparticles Designed for X-Ray Radiotherapy and Boron Neutron Capture Therapy Assisted by CT Imaging

New multifunctional theranostic vectors allow the expansion of cancer therapeutic approaches toward scarcely investigated fields. One example is the combination of boron neutron capture therapy (BNCT) and X-ray radiotherapy (XRT) for treating normal and XRT-resistant hypoxic tumor regions and reduce recurrence. Of great relevance for BNCT is also the support of viable, rapid, safe, and reliable techniques for the localization and quantification of the radiosensitizers in the tissues. To address these challenges, polymer-coated Au-B nanoparticles (NPs) are obtained starting from a laser ablation in liquid process. Despite thermodynamic constraints, the two elements coexist by short-range boron segregation in the gold lattice, as demonstrated experimentally and explained with the support of density functional theory calculations. Thus, the Au-B NPs maintain a marked gold character such as biocompatibility, stability, and straightforward surface chemistry with thiolated compounds, desirable for the integration with agents capable of cell targeting and internalization. Overall, the Au-B NPs exhibit the appropriate features for the investigation of combined BNCT and XRT, supported by the localization and quantification with X-ray computed tomography imaging. Besides, the Au-B nanotechnology tool is achievable without renouncing to reproducibility, environmental sustainability, and cost affordability thanks to the laser-assisted synthetic pathway.