Scale robust IMU-assisted KLT for stereo visual odometry solution

We propose a novel stereo visual IMU-assisted (Inertial Measurement Unit) technique that extends to large inter-frame motion the use of KLT tracker (Kanade–Lucas–Tomasi). The constrained and coherent inter-frame motion acquired from the IMU is applied to detected features through homogenous transform using 3D geometry and stereoscopy properties. This predicts efficiently the projection of the optical flow in subsequent images. Accurate adaptive tracking windows limit tracking areas resulting in a minimum of lost features and also prevent tracking of dynamic objects. This new feature tracking approach is adopted as part of a fast and robust visual odometry algorithm based on double dogleg trust region method. Comparisons with gyro-aided KLT and variants approaches show that our technique is able to maintain minimum loss of features and low computational cost even on image sequences presenting important scale change. Visual odometry solution based on this IMU-assisted KLT gives more accurate result than INS/GPS solution for trajectory generation in certain context

B-HoD: A Lightweight and Fast Binary Descriptor for 3D Object Recognition and Registration

3D object recognition and registration in computer vision applications has lately drawn much attention as it is capable of superior performance compared to its 2D counterpart. Although a number of high performing solutions do exist, it is still challenging to further reduce processing time and memory requirements to meet the needs of time critical applications. In this paper we propose an extension of the 3D descriptor Histogram of Distances (HoD) into the binary domain named the Binary-HoD (B-HoD). Our binary quantization procedure along with the proposed preprocessing step reduce an order of magnitude both processing time and memory requirements compared to current state of the art 3D descriptors. Evaluation on two popular low quality datasets shows its promising performance

Stixel Based Scene Understanding for Autonomous Vehicles

We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors

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

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

SAR automatic target recognition based on convolutional neural networks

We propose a multi-modal multi-discipline strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) imagery. Our architecture relies on a pre-trained, in the RGB domain, Convolutional Neural Network that is innovatively applied on SAR imagery, and is combined with multiclass Support Vector Machine classification. The multi-modal aspect of our architecture enforces the generalisation capabilities of our proposal, while the multi-discipline aspect bridges the modality gap. Even though our technique is trained in a single depression angle of 17°, average performance on the MSTAR database over a 10-class target classification problem in 15°, 30° and 45° depression is 97.8%. This multi-target and multi-depression ATR capability has not been reported yet in the MSTAR database literature

Using infrared based relative navigation for active debris removal

A debris-free space environment is becoming a necessity for current and future missions and activities planned in the coming years. The only means of sustaining the orbital environment at a safe level for strategic orbits (in particular Sun Synchronous Orbits, SSO) in the long term is by carrying out Active Debris Removal (ADR) at the rate of a few removals per year. Infrared (IR) technology has been used for a long time in Earth Observations but its use for navigation and guidance has not been subject of research and technology development so far in Europe. The ATV-5 LIRIS experiment in 2014 carrying a Commercial-of-The-Shelf (COTS) infrared sensor was a first step in de-risking the use of IR technology for objects detection in space. In this context, Cranfield University, SODERN and ESA are collaborating on a research to investigate the potential of IR-based relative navigation for debris removal systems. This paper reports the findings and developments in this field till date and the contributions from the three partners in this research

Thermal analysis of space debris for infrared based active debris removal

In space, visual based relative navigation systems suffer from dynamic illumination conditions of the target (Eclipse conditions, solar glare...etc.) where most of these issues are addressed by advanced mission planning techniques. However, such planning would not be always feasible or even if it is, it would not be straightforward for Active Debris Removal (ADR) missions. On the other hand, using an infrared based system would overcome this problem, if a guideline to predict infrared signature of space debris based on the target thermal profile could be provided for algorithm design and testing. Spacecraft thermal design is unique to every platform. This means every ADR target will have a different infrared signature which changes over time not just only due to orbital dynamics but also due to its thermal surface coatings. In order to provide a space debris infrared signature guideline for most of the possible ADR targets, we introduce an innovative grouping system for thermal surface coatings based on their behaviour in Space environment. Through the use of this grouping system, we propose a space debris infrared signature estimation method which was extensively verified by our simulations and experiments. During our verifications, we have also found out very important problem so called ”Signature Ambiguity” that is unique to Infrared Based Active Debris Removal (IR-ADR) systems which we have also discussed in our work

Using infrared based relative navigation for active debris removal

A debris-free space environment is becoming a necessity for current and future missions and activities planned in the coming years. The only means of sustaining the orbital environment at a safe level for strategic orbits (in particular Sun Synchronous Orbits, SSO) in the long term is by carrying out Active Debris Removal (ADR) at the rate of a few removals per year. Infrared (IR) technology has been used for a long time in Earth Observations but its use for navigation and guidance has not been subject of research and technology development so far in Europe. The ATV-5 LIRIS experiment in 2014 carrying a Commercial-of-The-Shelf (COTS) infrared sensor was a first step in de-risking the use of IR technology for objects detection in space. In this context, Cranfield University, SODERN and ESA are collaborating on a research to investigate the potential of IR-based relative navigation for debris removal systems. This paper reports the findings and developments in this field till date and the contributions from the three partners in this research

Local feature based automatic target recognition for future 3D active homing seeker missiles

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes