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.Comment: AIAA SciTech Forum 2023, 13 pages, 9 figure

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.Comment: 49 pages, 22 figure

HARDWARE IMPLEMENTATION OF NAVIGATION FILTERS FOR AUTOMATION OF DYNAMICAL SYSTEMS

To realize the full potential of autonomous navigation, computing at the edge of network infrastructure is critical in reducing response times. In navigation systems, high-performance computing resources are often necessary for state estimation. A hardware-focused approach using Field Programmable Gate Array (FPGA) for embedded design is presented to carry out high-performance edge computing required for advanced navigation of aerospace vehicles. FPGA architectures relieve the burden of storage-cum-processing of the measurement data while presenting a cost-optimized and accelerated infrastructure for navigation filter implementations. Leveraging the utility of software programming tools and the performance of customized hardware architecture, an FPGA-based hardware/software (HW/SW) codesign means for navigation filters is studied. The codesign splits a filter algorithm into tasks to be executed by the software and the hardware platforms. Transcendental function evaluations involved in a filter algorithm are tasked for software execution while a hardware platform implements the filter-specific logic at high speed. Three different navigation filter algorithms for embedded system design are implemented in this thesis: (a) Moving average filter for single-degree-of-freedom acceleration estimation, (b) Optimal Linear Translation and Attitude Estimator (OLTAE) for six-degrees-of-freedom pose estimation, and (c) Interferometric Vision-Based Navigation (iVisNav) for six-degrees-of-freedom relative motion rate estimation. The results of these hardware-based filter implementations demonstrate the capabilities of a custom embedded system design in a high-performance computational environment. The challenges involved in scientific computations using corresponding FPGA implementations of filter algorithms are analyzed and presented