Pose Estimation and Tracking for ASIST
- URL: http://arxiv.org/abs/2311.18665v1
- Date: Thu, 30 Nov 2023 16:15:29 GMT
- Title: Pose Estimation and Tracking for ASIST
- Authors: Ari Goodman, Gurpreet Singh, Ryan O'Shea, Peter Teague, James Hing
- Abstract summary: Aircraft Ship Integrated Secure and Traverse (ASIST) is a system designed to arrest helicopters safely and efficiently on ships.
PETA (Pose Estimation and Tracking for ASIST) is a research effort to create a helicopter tracking system prototype without hardware installation requirements for ASIST system operators.
PETA demonstrated the potential for state-of-the-art computer vision algorithms Faster R-CNN and HRNet to be used to estimate the pose of helicopters in real-time.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Aircraft Ship Integrated Secure and Traverse (ASIST) is a system designed to
arrest helicopters safely and efficiently on ships. Originally, a precision
Helicopter Position Sensing Equipment (HPSE) tracked and monitored the position
of the helicopter relative to the Rapid Securing Device (RSD). However, using
the HPSE component was determined to be infeasible in the transition of the
ASIST system due to the hardware installation requirements. As a result,
sailors track the position of the helicopters with their eyes with no sensor or
artificially intelligent decision aid. Manually tracking the helicopter takes
additional time and makes recoveries more difficult, especially at high sea
states. Performing recoveries without the decision aid leads to higher
uncertainty and cognitive load. PETA (Pose Estimation and Tracking for ASIST)
is a research effort to create a helicopter tracking system prototype without
hardware installation requirements for ASIST system operators. Its overall goal
is to improve situational awareness and reduce operator uncertainty with
respect to the aircrafts position relative to the RSD, and consequently
increase the allowable landing area. The authors produced a prototype system
capable of tracking helicopters with respect to the RSD. The software included
a helicopter pose estimation component, camera pose estimation component, and a
user interface component. PETA demonstrated the potential for state-of-the-art
computer vision algorithms Faster R-CNN and HRNet (High-Resolution Network) to
be used to estimate the pose of helicopters in real-time, returning ASIST to
its originally intended capability. PETA also demonstrated that traditional
methods of encoder-decoders could be used to estimate the orientation of the
helicopter and could be used to confirm the output from HRNet.
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