Multi-view monocular pose estimation for spacecraft relative navigation

This paper presents a method of estimating the pose of a non-cooperative target for spacecraft rendezvous applications employing exclusively a monocular camera and a threedimensional model of the target. This model is used to build an offline database of prerendered keyframes with known poses. An online stage solves the model-to-image registration problem by matching two-dimensional point and edge features from the camera to the database. We apply our method to retrieve the motion of the now inoperational satellite ENVISAT. The combination of both feature types is shown to produce a robust pose solution even for large displacements respective to the keyframes which does not rely on real-time rendering, making it attractive for autonomous systems applications

Histogram of distances for local surface description

3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising 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

Autonomous navigation for mobility scooters: a complete framework based on open-source software

In recent years, there has been a growing demand for small vehicles targeted at users with mobility restrictions and designed to operate on pedestrian areas. The users of these vehicles are generally required to be in control for the entire duration of their journey, but a lot more people could benefit from them if some of the driving tasks could be automated. In this scenario, we set out to develop an autonomous mobility scooter, with the aim to understand the commercial feasibility of a similar product. This paper reports on the progress of this project, proposing a framework for autonomous navigation on pedestrian areas, and focusing in particular on the construction of suitable costmaps. The proposed framework is based on open-source software, including a library created by the authors for the generation of costmaps

Robust Adversarial Attacks Detection for Deep Learning based Relative Pose Estimation for Space Rendezvous

Research on developing deep learning techniques for autonomous spacecraft relative navigation challenges is continuously growing in recent years. Adopting those techniques offers enhanced performance. However, such approaches also introduce heightened apprehensions regarding the trustability and security of such deep learning methods through their susceptibility to adversarial attacks. In this work, we propose a novel approach for adversarial attack detection for deep neural network-based relative pose estimation schemes based on the explainability concept. We develop for an orbital rendezvous scenario an innovative relative pose estimation technique adopting our proposed Convolutional Neural Network (CNN), which takes an image from the chaser's onboard camera and outputs accurately the target's relative position and rotation. We perturb seamlessly the input images using adversarial attacks that are generated by the Fast Gradient Sign Method (FGSM). The adversarial attack detector is then built based on a Long Short Term Memory (LSTM) network which takes the explainability measure namely SHapley Value from the CNN-based pose estimator and flags the detection of adversarial attacks when acting. Simulation results show that the proposed adversarial attack detector achieves a detection accuracy of 99.21%. Both the deep relative pose estimator and adversarial attack detector are then tested on real data captured from our laboratory-designed setup. The experimental results from our laboratory-designed setup demonstrate that the proposed adversarial attack detector achieves an average detection accuracy of 96.29%

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

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

SAR image dataset of military ground targets with multiple poses for ATR

Automatic Target Recognition (ATR) is the task of automatically detecting and classifying targets. Recognition using Synthetic Aperture Radar (SAR) images is interesting because SAR images can be acquired at night and under any weather conditions, whereas optical sensors operating in the visible band do not have this capability.Existing SAR ATR algorithms have mostly been evaluated using the MSTAR dataset.1 The problem with the MSTAR is that some of the proposed ATR methods have shown good classification performance even when targets were hidden,2 suggesting the presence of a bias in the dataset. Evaluations of SAR ATR techniques arecurrently challenging due to the lack of publicly available data in the SAR domain. In this paper, we present a high resolution SAR dataset consisting of images of a set of ground military target models taken at various aspect angles, The dataset can be used for a fair evaluation and comparison of SAR ATR algorithms. We applied the Inverse Synthetic Aperture Radar (ISAR) technique to echoes from targets rotating on a turntable and illuminated with a stepped frequency waveform. The targets in the database consist of four variants of two 1.7m-long models of T-64 and T-72 tanks. The gun, the turret position and the depression angle are varied to form 26 different sequences of images. The emitted signal spanned the frequency range from 13 GHz to 18 GHz to achieve a bandwidth of 5 GHz sampled with 4001 frequency points. The resolution obtained with respect to the size of the model targets is comparable to typical values obtained using SAR airborne systems. Single polarized images (Horizontal-Horizontal) are generated using the backprojection algorithm.3 A total of 1480 images are produced using a 20° integration angle. The images in the dataset are organized in a suggested training and testing set to facilitate a standard evaluation of SAR ATR algorithms