Skip to content
aiqbench takeMachine Learning

aiqbench analysis

This paper introduces NaRPA, a scientific ray-tracing engine designed to generate virtual space-borne datasets for testing aerospace visual navigation algorithms. By simulating passive and active vision sensors alongside LiDAR models, the framework supports the design and verification of robust navigation pipelines in complex orbital environments. The work represents a significant advancement in computer graphics applied to robotics and autonomous spacecraft operations rather than consumer AI applications.
Source coveringarXiv
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

NaRPA: Navigation and Rendering Pipeline for Astronautics

arXiv2 min read

arXiv:2211.01566v2 Announce Type: replace-cross Abstract: This paper presents the applications of scientific ray-tracing in modeling and simulating light transport for space-borne image data generation. A ray-tracing engine, the Navigation and Rendering Pipeline for Astronautics (NaRPA), is introduced as a rendering framework to generate virtual datasets and support simulations for robust navigation pipelines. Sensor and environment models that enable the synthesis of space-to-space and ground-to-space virtual observations are presented. The work demonstrates the capabilities of simulating passive and active vision-based sensors using NaRPA to facilitate the design, testing, and verification of aerospace visual navigation algorithms. Additionally, the paper describes a velocimeter LiDAR model and its statistical validation with experimental data.

Abstract:This paper presents the applications of scientific ray-tracing in modeling and simulating light transport for space-borne image data generation. A ray-tracing engine, the Navigation and Rendering Pipeline for Astronautics (NaRPA), is introduced as a rendering framework to generate virtual datasets and support simulations for robust navigation pipelines. Sensor and environment models that enable the synthesis of space-to-space and ground-to-space virtual observations are presented. The work demonstrates the capabilities of simulating passive and active vision-based sensors using NaRPA to facilitate the design, testing, and verification of aerospace visual navigation algorithms. Additionally, the paper describes a velocimeter LiDAR model and its statistical validation with experimental data.

Graphics (cs.GR); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2211.01566 [cs.GR]

(or arXiv:2211.01566v2 [cs.GR] for this version)

https://doi.org/10.48550/arXiv.2211.01566 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 203 words

Related coverage