Title:
Closed Loop Perception for Resource Efficient Autonomous Systems
Closed Loop Perception for Resource Efficient Autonomous Systems
Author(s)
Samal, Kruttidipta
Advisor(s)
Mukhopadhyay, Saibal
Wolf, Marilyn
Wolf, Marilyn
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Abstract
Autonomous Systems such as Autonomous Vehicles (AV), robots and drones are being developed for large scale deployments in real world applications such as transportation, agriculture, defense, urban planning etc. To operate safely in such diverse and dynamic scenarios, the perception engine within these systems must be capable of adapting to the dynamic real-time constraints such as latency and energy consumption. This adaptability is not present in the modern perception systems as they are open-loop by design and therefore neither aware nor capable of reacting to the dynamics of a real-world scenario. In this thesis we present the Closed Loop Perception that interprets the perception process in modern autonomous systems as a control system. We first create the notion of `perception risk' which represents the state of the process by estimating perception failures and then propose a risk-resource controller that generates feedback signals to dynamically control the resource consumption within the system. The proposed Closed Loop Perception System can introspect and adapt to the real-time requirements of an Autonomous System operating in the wild.
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Date Issued
2022-01-14
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Text
Resource Subtype
Dissertation