Spacecraft Relative Navigation Research Articles

Monocular SLAM with Point and Line Features Applied to Spacecraft Relative Navigation

Monocular SLAM with Point and Line Features Applied to Spacecraft Relative Navigation

Robust spacecraft relative pose estimation via CNN-aided line segments detection in monocular images

Autonomous spacecraft relative navigation via monocular images became a hot topic in the past few years and, recently, received a further push thanks to the constantly growing field of artificial neural networks and the publication of several spaceborne image datasets. Despite the proliferation of spacecraft relative-state initialization algorithms developed, most architectures adopt computationally expensive solutions relying on convolutional neural networks (CNNs) that provide accurate output at the cost of a high computational burden that seems unfeasible for current spaceborne hardware. The paper addresses this issue by proposing a novel pose initialization algorithm based on lightweight CNNs. Inspired by previous state-of-the-art algorithms, the developed architecture leverages a fast and accurate target detection CNN followed by a line segment detection CNN capable of running with low inference time on mobile devices. The line segments and their junctions are grouped into complex geometrical groups, reducing the solution search space, and subsequently, they are adopted to extract the final pose estimate. As a main outcome, the analyses demonstrate that the lightweight architecture developed scores high accuracy in the pose estimation task, with a mean estimation error of less than 10 cm in translation and 2.5°in rotation. The baseline algorithm scores a mean SLAB error of 0.04552 with a standard deviation of 0.22972 in the test dataset. Detailed analyses demonstrate that the uncertainties on the overall pose score are driven mainly by errors in the relative attitude, which gives the highest contribution to the pose error metric adopted. The analyses on the error distributions point out that the uncertainties on the estimated relative position are higher in the camera boresight axis direction. Concerning the relative attitude, the algorithm proposed has higher uncertainties in estimating directions of the target x and y axes due to ambiguities related to the target geometry. Notably, the target detection CNN trained in this work outperforms the previous top scores in the benchmark dataset. The performances of the proposed algorithm have been investigated further by analyzing the effects on the accuracy due to the relative distance and the presence of background in the images. Lastly, the paper delves into the possibility of adopting a sub-portion of the 2D-to-3D match matrix made by the most complex perceptual groups identified that positively affects the overall run-time, pointing out the performances in terms of accuracy of the estimates and providing a comparison of both the baseline and the reduced match matrix versions against state-of-the-art algorithms concerning relative position and attitude errors and solution availability, highlighting the high accuracy and solution availability of the proposed architectures.

Optimization-based iterative and robust strategy for spacecraft relative navigation in elliptical orbit

The nonlinear relative dynamics in elliptical orbits and heavy-tailed observation noise can induce a noticeable degradation in navigation accuracy. Although the extensively applied Huber-based M-estimation can cope with non-Gaussian noise, the iterative strategy employed fails to re-linearize the observation model using posterior estimate, and this can induce degradation in filtering efficiency. Considering the inadequate research on iterative and robust strategies, we combine iterated unscented Kalman filter (IUKF) with generalized M-estimation, thereby uniformly and strictly deriving an optimization-based iterative and robust strategy. To approximate the nonlinear observation model more exactly, statistical linearization regression is introduced in the Gauss-newton method, by which the uncertainty induced by approximation errors is compensated in observation noise covariance. Further, we improve the IUKF-based iterative and robust strategy by presenting a novel cost function, where the modified covariance matrix from the previous iteration is incorporated again in the next iteration loop. Comparative studies under non-Gaussian noise show that the novel strategy can facilitate the estimator to achieve higher filtering accuracy, stronger robustness and better consistency. Therefore, the proposed strategy is more suitable for the robust filtering framework.

An improved adaptive robust information filter for spacecraft relative navigation

This work concentrates on addressing the problem of time-varying measurement noise covariance and outliers (contaminated Gaussian distribution), which can significantly deteriorate the estimation accuracy of conventional Kalman filter. Adaptive filters or robust filters are only suitable for solving one of the above-mentioned problems. Meanwhile, the existing adaptive robust filters, performing unsatisfactorily, also can not meet the requirements of spacecraft relative navigation, which needs an accurate and reliable filter. This paper proposes an improved adaptive robust information filter, referred to as variational Bayesian (VB) adaptive dynamic-covariance-scaling (DCS)-based cubature information filter (VB-DCSCIF). The proposed filter, based on the cubature information filter framework, uses VB approximation to track measurement noise covariance with time-varying statistical characteristics, and relies on the improved DCS kernel function to suppress outliers in measurements. Therefore, VB-DCSCIF incorporates the advantages of VB approximation and the improved DCS kernel, exhibiting adaptivity and robustness. Spacecraft relative navigation simulations and Monte Carlo simulations demonstrate that VB-DCSCIF outperforms other filters for providing high-precision state estimation under time-varying measurement noise covariance and outliers.

Monocular-Based Pose Estimation Based on Fiducial Markers for Space Robotic Capture Operations in GEO

This paper tackles the problem of spacecraft relative navigation to support the reach and capture of a passively cooperative space target using a chaser platform equipped with a robotic arm in the frame of future operations such as On Orbit Servicing and Active Debris Removal. Specifically, it presents a pose determination architecture based on monocular cameras to deal with a space target in GEO equipped with retro-reflective and black-and-white fiducial markers. The proposed architecture covers the entire processing pipeline, i.e., starting from markers’ detection and identification up to pose estimation by solving the Perspective-n-Points problem with a customized implementation of the Levenberg–Marquardt algorithm. It is designed to obtain relative position and attitude measurements of the target’s main body with respect to the chaser, as well as of the robotic arm’s end effector with respect to the selected grasping point. The design of the configuration of fiducial markers to be installed on the target’s approach face to support the pose determination task is also described. A performance assessment is carried out by means of numerical simulations using the Planet and Asteroid Natural Scene Generation Utility tool to produce realistic synthetic images of the target. The proposed approach robustness is evaluated against variable illumination scenarios and considering different uncertainty levels in the knowledge of initial conditions and camera intrinsic parameters.

Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.

Spacecraft relative navigation with an omnidirectional vision sensor

With the onset of autonomous spacecraft formation flying missions, the ability of satellites to autonomously navigate relatively to other space objects has become essential. To implement spacecraft relative navigation, relative measurements should be taken, and processed using relative state estimation. An efficient way to generate such information is by using vision-based measurements. Cameras are passive, low-energy, and information-rich sensors that do not actively interact with other space objects. However, pointing cameras with a conventional field-of-view to other space objects requires much a-priori initialization data; in particular, dedicated attitude maneuvers are needed, which may interfere with the satellite’s main mission. One way to overcome these difficulties is to use an omnidirectional vision sensor, which has a 360-degree horizontal field of view. In this work, we present the development of an omnidirectional vision sensor for satellites, which can be used for spacecraft relative navigation, formation flying, and space situational awareness. The study includes the development of the measurement equations, dynamical models, and state estimation algorithms, as well as a numerical study, an experimental investigation, and a space scalability analysis.

Radial basis function neural network aided adaptive extended Kalman filter for spacecraft relative navigation

This paper presents a novel technique, combining neural network and Kalman filter, for state estimation. The proposed solution provides the estimates of the system states while also estimating the uncertain or unmodeled terms of the process dynamics. The developed algorithm exploits a Radial Basis Function Neural Network that outputs an estimate of the disturbances that are included in the prediction step of an Adaptive Extended Kalman Filter. A recursive form of adaptation is used to limit the computational burden. The proposed solution is compared to classical navigation filter implementations. A realistic spacecraft relative navigation scenario is selected to test the filter performance. Simulations are performed with accurate tuning and also in off-nominal conditions to test the filter robustness.

H ∞ LIDAR odometry for spacecraft relative navigation

Current light detection and ranging (LIDAR) based odometry solutions that are used for spacecraft relative navigation suffer from quite a few deficiencies. These include an off-line training requirement and relying on the iterative closest point (ICP) that does not guarantee a globally optimum solution. To encounter this, the authors suggest a robust architecture that overcomes the problems of current proposals by combining the concepts of 3D local feature matching with an adaptive variant of the H∞ recursive filtering process. Trials on real laser scans of an EnviSat model demonstrate that the proposed architecture affords at least one order of magnitude better accuracy compared to ICP.

Uncooperative Spacecraft Relative Navigation With LIDAR-Based Unscented Kalman Filter

Autonomous relative navigation is a critical functionality which needs to be developed to enable safe maneuvers of a servicing spacecraft (chaser) in close-proximity with respect to an uncooperative space target, in the frame of future On-Orbit Servicing or Active Debris Removal missions. Due to the uncooperative nature of the target, in these scenarios, relative navigation is carried out exploiting active or passive Electro-Optical sensors mounted on board the chaser. The focus here is placed on active systems, e.g., LIDARs. In this paper, an original loosely-coupled relative navigation architecture which integrates pose determination algorithms designed to process raw LIDAR data (i.e., 3D point clouds) within a Kalman filtering scheme is presented. Pose determination algorithms play a twofold role being used to initialize the filter state and covariance as well as in the update phase of the Kalman filter. The proposed filtering scheme is an Unscented Kalman Filter designed to use, as measurements for the update phase, relative position, attitude and angular velocity estimates. Performance assessment is carried out within a simulation environment realistically reproducing the operation of a scanning LIDAR and the relative motion between two spacecraft during a target monitoring maneuver. The numerical simulation campaign demonstrates robustness of the proposed approach even when dealing with challenging conditions (e.g., low range measurement accuracy, low update rate and high point-cloud sparseness) determined by the LIDAR noise level and operational parameters.

A Concise Guide to Feature Histograms with Applications to LIDAR-Based Spacecraft Relative Navigation

With the availability and popularity of 3D sensors, it is advantageous to re-examine the use of point cloud descriptors for the purpose of pose estimation and spacecraft relative navigation. One popular descriptor is the oriented unique repeatable clustered viewpoint feature histogram (OUR-CVFH), which is most often utilized in personal and industrial robotics to simultaneously recognize and navigate relative to an object. Recent research into using the OUR-CVFH descriptor for spacecraft navigation has produced favorable results. Since OUR-CVFH is the most recent innovation in a large family of feature histogram point cloud descriptors, discussions of parameter settings and insights into its functionality are spread among various publications and online resources. This paper organizes the history of feature histogram point cloud descriptors for a straightforward explanation of their evolution. This article compiles all the requisite information needed to implement OUR-CVFH into one location, as well as providing useful suggestions on how to tune the generation parameters. This work is beneficial for anyone interested in using this histogram descriptor for object recognition or navigation – may it be personal robotics or spacecraft navigation.

A New Feature Points Reconstruction Method in Spacecraft Vision Navigation

The important applications of monocular vision navigation in aerospace are spacecraft ground calibration tests and spacecraft relative navigation. Regardless of the attitude calibration for ground turntable or the relative navigation between two spacecraft, it usually requires four noncollinear feature points to achieve attitude estimation. In this paper, a vision navigation system based on the least feature points is designed to deal with fault or unidentifiable feature points. An iterative algorithm based on the feature point reconstruction is proposed for the system. Simulation results show that the attitude calculation of the designed vision navigation system could converge quickly, which improves the robustness of the vision navigation of spacecraft.

Adaptive sparse grid quadrature filter for spacecraft relative navigation

This paper explores a novel adaptive sparse grid quadrature filter. The sparse grid quadrature approach has been recently developed for nonlinear estimation problems to alleviate the curse-of-dimensionality issue of the Gauss–Hermite quadrature filter. Accuracy level of the sparse grid quadrature filter is an important tuning factor that affects desired performance. The proposed filter autonomously adjusts the accuracy level of the sparse grid quadrature rule in both prediction and update steps by increasing the level gradually until an adaptation criterion is satisfied. The adaptation criterion is derived based on a quadrature error estimator. The nestedness property of sparse grid quadrature rule enables efficient computation in adaptation by reusing quadrature points of the previous level sparse grid quadrature. An application to spacecraft relative navigation has been made to demonstrate the adaptive spare grid quadrature filter outperforming the extended Kalman filter and the unscented Kalman filter.

Square-root sigma-point Kalman filtering for spacecraft relative navigation

Abstract A variant of sigma-point Kalman filters family called square-root unscented Kalman filter is derived to estimate the relative attitude and position of two spacecrafts referred to as the leader and follower. The square-root forms of unscented Kalman filter have a consistently increased numerical stability because all resulting covariance matrices are guaranteed to stay semi-positive definite. The general six degrees of freedom relative equations of motion are developed based upon the tensors. All leader states are assumed known, whereas the relative states are estimated using available line-of-sight observations between the vehicles along with acceleration and angular velocity measurements of the follower. The quaternion is used to describe the spacecraft relative attitude kinematics, while a three-dimensional generalized Rodrigues parameter is used to maintain the quaternion normalization constraint in the filter formulation. The simulation results indicate that the proposed filter can provide lower relative attitude and position estimation errors with faster convergence rates than the standard extended Kalman filter.

Modeling, Analysis, and Simulation of GPS Carrier Phase for Spacecraft Relative Navigation

Models of GPS carrier phase measurements have been derived, analyzed, and used to develop an off-line simulator. This work is being carried out to support the development of Carrier phase Differential GPS (CDGPS) systems for relative navigation of formations of spacecraft that orbit in GEO and HEO. Carrier phase models are built up by considering signal generation on the GPS satellite and signal processing in the user receiver. An analysis of measured carrier phase has been used to develop receiver design guidelines which guarantee that a receiver's outputs can be used by CDGPS algorithms that resolve carrier phase ambiguities as exact integers. The carrier phase simulation includes errors such as incorrect GPS ephemerides, thermal noise, multi-path, circular-polarization-induced phase wind-up, and altitude-dependent ionospheric delay. Simulation results demonstrate the efficacy and limitations of CDGPS for use in relative navigation of spacecraft formations.

A real-time kinematic GPS sensor for spacecraft relative navigation

The concept and prototype implementation of a spaceborne relative navigation sensor based on a pair of GPS receivers is presented. It employs two individual receivers exchanging raw measurements via a dedicated serial data link. Besides computing their own navigation solution, the receivers process single difference measurements to obtain their mutual relative state. The differential processing provides a high level of common error cancellation while the resulting noise is minimized by appropriate use of carrier phase measurements. A prototype relative navigation sensor making use of the above concepts has been built up based on the GPS Orion 12 channel L1 receiver and qualified in hardware-in-the-loop tests using a GPS signal simulator. It provides a relative navigation solution with representative r.m.s. accuracies of 0.5 m and 1 cm/s, respectively, for position and velocity. For ease of use the relative state is provided in a co-moving frame aligned with the radial, cross-track and along-track direction. The purely kinematic nature of state estimation and the small latency make the system well suited for maneuvering spacecraft, while the minimalist hardware requirements facilitate its use on microsatellite formations.