Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy

Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.

Deep Reinforcement Learning for Autonomous Spacecraft Inspection using Illumination

This paper investigates the problem of on-orbit spacecraft inspection using a single free-flying deputy spacecraft, equipped with an optical sensor, whose controller is a neural network control system trained with Reinforcement Learning (RL). This work considers the illumination of the inspected spacecraft (chief) by the Sun in order to incentivize acquisition of well-illuminated optical data. The agent's performance is evaluated through statistically efficient metrics. Results demonstrate that the RL agent is able to inspect all points on the chief successfully, while maximizing illumination on inspected points in a simulated environment, using only low-level actions. Due to the stochastic nature of RL, 10 policies were trained using 10 random seeds to obtain a more holistic measure of agent performance. Over these 10 seeds, the interquartile mean (IQM) percentage of inspected points for the finalized model was 98.82%.

Differentiable Rendering for Pose Estimation in Proximity Operations

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.

NaRPA: Navigation and Rendering Pipeline for Astronautics

This paper presents Navigation and Rendering Pipeline for Astronautics (NaRPA) - a novel ray-tracing-based computer graphics engine to model and simulate light transport for space-borne imaging. NaRPA incorporates lighting models with attention to atmospheric and shading effects for the synthesis of space-to-space and ground-to-space virtual observations. In addition to image rendering, the engine also possesses point cloud, depth, and contour map generation capabilities to simulate passive and active vision-based sensors and to facilitate the designing, testing, or verification of visual navigation algorithms. Physically based rendering capabilities of NaRPA and the efficacy of the proposed rendering algorithm are demonstrated using applications in representative space-based environments. A key demonstration includes NaRPA as a tool for generating stereo imagery and application in 3D coordinate estimation using triangulation. Another prominent application of NaRPA includes a novel differentiable rendering approach for image-based attitude estimation is proposed to highlight the efficacy of the NaRPA engine for simulating vision-based navigation and guidance operations.

FPGA Hardware Acceleration for Feature-Based Relative Navigation Applications

Estimation of rigid transformation between two point clouds is a computationally challenging problem in vision-based relative navigation. Targeting a real-time navigation solution utilizing point-cloud and image registration algorithms, this paper develops high-performance avionics for power and resource constrained pose estimation framework. A Field-Programmable Gate Array (FPGA) based embedded architecture is developed to accelerate estimation of relative pose between the point-clouds, aided by image features that correspond to the individual point sets. At algorithmic level, the pose estimation method is an adaptation of Optimal Linear Attitude and Translation Estimator (OLTAE) for relative attitude and translation estimation. At the architecture level, the proposed embedded solution is a hardware/software co-design that evaluates the OLTAE computations on the bare-metal hardware for high-speed state estimation. The finite precision FPGA evaluation of the OLTAE algorithm is compared with a double-precision evaluation on MATLAB for performance analysis and error quantification. Implementation results of the proposed finite-precision OLTAE accelerator demonstrate the high-performance compute capabilities of the FPGA-based pose estimation while offering relative numerical errors below 7%.

An FPGA framework for Interferometric Vision-Based Navigation (iVisNav)

Interferometric Vision-Based Navigation (iVisNav) is a novel optoelectronic sensor for autonomous proximity operations. iVisNav employs laser emitting structured beacons and precisely characterizes six degrees of freedom relative motion rates by measuring changes in the phase of the transmitted laser pulses. iVisNav's embedded package must efficiently process high frequency dynamics for robust sensing and estimation. A new embedded system for least squares-based rate estimation is developed in this paper. The resulting system is capable of interfacing with the photonics and implement the estimation algorithm in a field-programmable gate array. The embedded package is shown to be a hardware/software co-design handling estimation procedure using finite precision arithmetic for high-speed computation. The accuracy of the finite precision FPGA hardware design is compared with the floating-point software evaluation of the algorithm on MATLAB to benchmark its performance and statistical consistency with the error measures. Implementation results demonstrate the utility of FPGA computing capabilities for high-speed proximity navigation using iVisNav.

Model-based Shape Control of Tensegrity Robotic Systems

This paper proposes a model-based approach to control the shape of a tensegrity system by driving its node position locations. The nonlinear dynamics of the tensegrity system is used to regulate position, velocity, and acceleration to the specified reference trajectory. State feedback control design is used to obtain the solution for the control variable as a linear programming problem. Shape control for the gyroscopic tensegrity systems is discussed, and it is observed that these systems increase the reachable space for the structure by providing independent control over certain rotational degrees of freedom. Disturbance rejection of the tensegrity system is further studied in the paper. A methodology to calculate the control gains to bound the errors for five different types of problems is provided. The formulation uses a Linear Matrix Inequality (LMI) approach to stipulate the desired performance bounds on the error for $\mathcal{H}_\infty$, generalized $\mathcal{H}_2$, LQR, covariance control and stabilizing control problem. A high degree of freedom tensegrity $T_2D_1$ robotic arm is used as an example to show the efficacy of the formulation.

Markov Data-Based Reference Tracking of Tensegrity Morphing Airfoils

This letter presents a data-based control design for reference tracking applications. This design finds the optimal control sequence, which minimizes a quadratic cost function consisting of tracking error and input increments over a finite interval [0,N]. The only information needed is the first N+1 Markov parameters of the system. This design is employed on a tensegrity morphing airfoil whose topology has been described in detail in this letter. A NACA 2412 airfoil with specified morphing targets is chosen to verify the developed design. The principle developed in this letter is also applicable to other structural control problems.

Bayesian Inference of Spacecraft Pose using Particle Filtering

Automated 3D pose estimation of satellites and other known space objects is a critical component of space situational awareness. Ground-based imagery offers a convenient data source for satellite characterization; however, analysis algorithms must contend with atmospheric distortion, variable lighting, and unknown reflectance properties. Traditional feature-based pose estimation approaches are unable to discover an accurate correlation between a known 3D model and imagery given this challenging image environment. This paper presents an innovative method for automated 3D pose estimation of known space objects in the absence of satisfactory texture. The proposed approach fits the silhouette of a known satellite model to ground-based imagery via particle filtering. Each particle contains enough information (orientation, position, scale, model articulation) to generate an accurate object silhouette. The silhouette of individual particles is compared to an observed image. Comparison is done probabilistically by calculating the joint probability that pixels inside the silhouette belong to the foreground distribution and that pixels outside the silhouette belong to the background distribution. Both foreground and background distributions are computed by observing empty space. The population of particles are resampled at each new image observation, with the probability of a particle being resampled proportional to how the particle's silhouette matches the observation image. The resampling process maintains multiple pose estimates which is beneficial in preventing and escaping local minimums. Experiments were conducted on both commercial imagery and on LEO satellite imagery. Imagery from the commercial experiments are shown in this paper.