Closed-loop Approach to Perception in Autonomous System

Currently, functional tasks within Autonomous Systems are balkanized into several sub-systems such as object detection, tracking, motion planning, multi-sensor fusion etc. which are developed and tested in isolation. In recent times, deep learning is used in the perception systems for improved accuracy, but such algorithms are not adaptive to the transient real-world requirements of an Autonomous System such as latency and energy. These limitations are critical for resource constrained systems such as autonomous drones. Therefore, a holistic closed-loop system design is required for building reliable and efficient perception systems for autonomous drones. The closed-loop perception system creates a focus-of-attention based feedback from end-task such as motion planning to control computation within the deep neural networks (DNNs) used in early perception tasks such as object detection. We observe that this closed-loop perception system improves resource utilization of resource hungry DNNs within perception system with minimal impact on motion planning.