High-speed multi-dimensional relative navigation for uncooperative space objects

This work proposes a high-speed Light Detection and Ranging (LIDAR) based navigation architecture that is appropriate for uncooperative relative space navigation applications. In contrast to current solutions that exploit 3D LIDAR data, our architecture transforms the odometry problem from the 3D space into multiple 2.5D ones and completes the odometry problem by utilizing a recursive filtering scheme. Trials evaluate several current state-of-the-art 2D keypoint detection and local feature description methods as well as recursive filtering techniques on a number of simulated but credible scenarios that involve a satellite model developed by Thales Alenia Space (France). Most appealing performance is attained by the 2D keypoint detector Good Features to Track (GFFT) combined with the feature descriptor KAZE, that are further combined with either the H∞ or the Kalman recursive filter. Experimental results demonstrate that compared to current algorithms, the GFTT/KAZE combination is highly appealing affording one order of magnitude more accurate odometry and a very low processing burden, which depending on the competitor method, may exceed one order of magnitude faster computation

Real-Time Setup with PD and Backstepping Control for a Pelican Quadrotor

In this paper, a real-time setup and an implementation of a Proportional Derivative (PD) controller for orientation and comparison between PD and BackStepping (BS) controllers for linear positioning are presented using a Pelican quadrotor from Ascending Technologies (AscTec). An onboard Inertial Measurement Unit (IMU) was used for orientation control and Optitrack Vision Tracking System for linear positioning control. A linear Kalman filter was implemented for linear velocity estimation. The software and hardware integration was achieved with the help of the Robot Operating System (ROS). Simulations and experiments with this drone platform are achieved in order to implement different controller algorithms and analyse them in order to achieve better aircraft performance

Unscented Kalman Filter for Vision Based Target Localisation with a Quadrotor

Unmanned aerial vehicles (UAV) equipped with a navigation system and an embedded camera can be used to estimate the position of a desired target. The relative position of the UAV along with knowledge of camera orientation and imagery data can be used to produce bearing measurements that allow estimation of target position. The filter methods applied are prone to biases due to noisy measurements. Further noise may be encountered depending on the UAV trajectory for target localisation. This work presents the implementation of an Unscented Kalman Filter (UKF) to estimate the position of a target on the 3D cartesian plane within a small indoor scenario. A small UAV with a single board computer, equipped with a frontal camera and moving in an oval trajectory at a fixed height was employed. Such a trajectory enabled an experimental comparison of UAV simulation data with UAV real-time flight data for indoor conditions. Optitrack Motion system and the Robot Operative System (ROS) were used to retrieve the drone position and exchange information at high rates

Evaluating 3D local descriptors for future LIDAR missiles with automatic target recognition capabilities

Future light detection and ranging seeker missiles incorporating 3D automatic target recognition (ATR) capabilities can improve the missile’s effectiveness in complex battlefield environments. Considering the progress of local 3D descriptors in the computer vision domain, this paper evaluates a number of these on highly credible simulated air-to-ground missile engagement scenarios. The latter take into account numerous parameters that have not been investigated yet by the literature including variable missile – target range, 6-degrees-of-freedom missile motion and atmospheric disturbances. Additionally, the evaluation process utilizes our suggested 3D ATR architecture that compared to current pipelines involves more post-processing layers aiming at further enhancing 3D ATR performance. Our trials reveal that computer vision algorithms are appealing for missile-oriented 3D ATR

Thermal stereo odometry for UAVs

In the last decade, visual odometry (VO) has attracted significant research attention within the computer vision community. Most of the works have been carried out using standard visible-band cameras. These sensors offer numerous advantages but also suffer from some drawbacks such as illumination variations and limited operational time (i.e., daytime only). In this paper, we explore techniques that allow us to extend the concepts beyond the visible spectrum. We introduce a localization solution based on a pair of thermal cameras. We focus on VO and demonstrate the accuracy of the proposed solution in daytime as well as night-time. The first challenge with thermal cameras is their geometric calibration. Here, we propose a solution to overcome this issue and enable stereopsis. VO requires a good set of feature correspondences. We use a combination of Fast-Hessian detector with for Fast Retina Keypoint descriptor for that purpose. A range of optimization techniques can be used to compute the incremental motion. Here, we propose the double dogleg algorithm and show that it presents an interesting alternative to the commonly used Levenberg-Marquadt approach. In addition, we explore thermal 3-D reconstruction and show that similar performance to the visible-band can be achieved. In order to validate the proposed solution, we build an innovative experimental setup to capture various data sets, where different weather and time conditions are considered

Certain subclasses of multivalent functions defined by new multiplier transformations

In the present paper the new multiplier transformations \mathrm{{\mathcal{J}% }}_{p}^{\delta }(\lambda ,\mu ,l) (\delta ,l\geq 0,\;\lambda \geq \mu \geq 0;\;p\in \mathrm{% }%\mathbb{N} )} of multivalent functions is defined. Making use of the operator $\mathrm{{\mathcal{J}}}_{p}^{\delta }(\lambda ,\mu ,l),$ two new subclasses $\mathcal{P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ and $\widetilde{\mathcal{P}}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$\textbf{\ }of multivalent analytic functions are introduced and investigated in the open unit disk. Some interesting relations and characteristics such as inclusion relationships, neighborhoods, partial sums, some applications of fractional calculus and quasi-convolution properties of functions belonging to each of these subclasses $\mathcal{P}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ and $\widetilde{\mathcal{P}}_{\lambda ,\mu ,l}^{\delta }(A,B;\sigma ,p)$ are investigated. Relevant connections of the definitions and results presented in this paper with those obtained in several earlier works on the subject are also pointed out

Role of dynamic Jahn-Teller distortions in Na2C60 and Na2CsC60 studied by NMR

Through 13C NMR spin lattice relaxation (T1) measurements in cubic Na2C60, we detect a gap in its electronic excitations, similar to that observed in tetragonal A4C60. This establishes that Jahn-Teller distortions (JTD) and strong electronic correlations must be considered to understand the behaviour of even electron systems, regardless of the structure. Furthermore, in metallic Na2CsC60, a similar contribution to T1 is also detected for 13C and 133Cs NMR, implying the occurence of excitations typical of JT distorted C60^{2-} (or equivalently C60^{4-}). This supports the idea that dynamic JTD can induce attractive electronic interactions in odd electron systems.Comment: 3 figure

FPGA-based multi-sensor relative navigation in space: Preliminary analysis in the framework of the I3DS H2020 project

The Horizon 2020 Integrated 3D Sensors (I3DS) project brings together the following entities throughout Europe: THALES ALENIA SPACE - France / Italy / UK / Spain, SINTEF (Norway), TERMA (Denmark), COSINE (Netherlands), PIAP Space (Poland), HERTZ Systems (Poland), and Cranfield University (UK). I3DS is co-funded under the Horizon 2020 EU research and development program and is part of the Strategic Research Cluster on Space Robotics Technologies. The ambition of I3DS is to produce a standardised modular Inspector Sensor Suite (INSES) for autonomous orbital and planetary applications for future space missions. Orbital applications encompass activities such as on-orbit servicing and repair, space rendezvous and docking, collision avoidance and active debris removal (ADR). Simultaneous localisation and surface mapping (SLAM) for planetary exploration and general navigation in an unknown environment for scientific purposes can be considered in planetary applications. These envisaged space applications can be tackled by exploiting the flexibility, high performance and long product life of FPGAs. Conventional FPGAs are subject to Single Event Upsets (SEU) due to space radiation, causing their failure. Therefore, space-graded FPGAs, such as those developed by Xilinx, are targeted within the I3DS project. Currently, the main use of the FPGA within the development of this robust end-to-end multi-sensor suite is for navigation and data preprocessing. The aim of this paper is to assess the capabilities of FPGAs to carry out complex operations, such as running navigation algorithms for space applications. The motivation for the development of the on-board software architecture is as follows: raw data, acquired from the various sensors – including, among others, a High Resolution camera, a stereo camera and a LiDAR – is pre-processed to ensure the provision of robust and optimised inputs to 3D navigation algorithms. Noise reduction and conversion into suitable formats for the successful application of navigation algorithms are therefore the main aims of the data pre-processing. Some techniques adopted in this phase include outlier rejection and data dimensionality reduction for large point clouds, e.g. from LiDAR, and geometric and radiometric correction of the images from the cameras. The pre-processed data will then feed state-of-the-art relative navigation algorithms. Some of the proposed navigation algorithms include Generalised Iterative Closest Point (GICP) for dense 3D point clouds, relative positioning with fiducial markers, and visual odometry. The system environment for the preliminary operation is a test-bench setup formed by a standard desktop computer and a non-space-graded FGA (Xilinx UltraZed-EG FPGA). The choice of FPGA was based on the similarity of this board to other spacegraded ones also provided by Xilinx. Experimental tests on the algorithms are being performed in the framework of the validation campaign for the I3DS project. Preliminary results indicate that the data pre-processing can be efficiently carried out on the FPGA board

Inclusion properties of certain subclasses of analytic functions defined by generalized Salagean operator

Let $A$ denote the class of analytic functions with the normalization $f(0)=f^{\prime }(0)-1=0$ in the open unit disc U=\{z:\left\vert z\right\vert <1\}.  Set $$f_{\lambda }^{n}(z)=z+\sum_{k=2}^{\infty }[1+\lambda (k-1)]^{n}z^{k}\quad(n\in N_{0};\ \lambda \geq 0;\ z\in U),$$ and define $f_{\lambda ,\mu }^{n}$ in terms of the Hadamard product f_{\lambda }^{n}(z)\ast f_{\lambda ,\mu }^{n}=\frac{z}{(1-z)^{\mu }}\quad (\mu >0;\ z\in U). In this paper, we introduce several subclasses of analytic functions defined by means of the operator $I_{\lambda ,\mu }^{n}:A\longrightarrow A$, given by I_{\lambda ,\mu }^{n}f(z)=f_{\lambda ,\mu }^{n}(z)\ast f(z)\quad (f\in A;\ n\in N_{0;}\ \lambda \geq 0;\ \mu >0). Inclusion properties of these classes and the classes involving the generalized Libera integral operator are also considered