High-integrity navigation for sensor-based 3D world modeling

Vladislav Gavrilets; Chris Boggs; Max Taylor

doi:10.1117/12.2518465

1 May 2019 High-integrity navigation for sensor-based 3D world modeling

Vladislav Gavrilets, Chris Boggs, Max Taylor

Proceedings Volume 11019, Situation Awareness in Degraded Environments 2019; 110190M (2019) https://doi.org/10.1117/12.2518465
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

There is emerging demand for multi-ship sensor-based 3D world modeling (3DWM) for providing safe flight guidance in degraded visual environments (DVE). For illustration, we consider the leader-follower scenario, where the leader platform has the perception sensor suite (e.g., LIDAR, RADAR, Optical Structure from Motion (SfM)) used to create a dynamic 3DWM, depicting objects in the environment. The 3DWM is then distributed to the follower platform, for use in pilot cueing in DVE to avoid obstacles or for pilot workload reduction. Leader navigation state errors can cause inaccurate placement of free/occupied space in the common mapping frame, which can lead to a safety-critical failure to avoid obstacles. To ensure safe obstacle avoidance, an overbound on the leader’s navigation errors relative to the common mapping frame must be known with high confidence. A similar overbound must also be established for the follower vehicle. To support this need for high-integrity navigation, this paper adapts existing high-integrity navigation standards as a framework for this scenario. Based on this framework, a sample error decomposition is performed. Potential navigation solutions that meet these example requirements are defined. Finally, modifications needed to the 3DWM measurement update process to accurately represent navigation uncertainty in the 3DWM are highlighted.

Conference Presentation

Citation Download Citation

Vladislav Gavrilets, Chris Boggs, and Max Taylor "High-integrity navigation for sensor-based 3D world modeling", Proc. SPIE 11019, Situation Awareness in Degraded Environments 2019, 110190M (1 May 2019); https://doi.org/10.1117/12.2518465

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