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Computer vision solution to picosatellite detection and pose estimation

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AeroCube

Brief

This repository more-or-less contains all functionality intended to be run on the Jetson TX1 board, and forms the backbone of our application.

Guide

A breakdown of the folders found at the root-level directory of the repository follows:

  • ImP - image processing module responsible for scanning an image and returning information about detected AeroCubes; also contains the data structures that represent an AeroCube and its members; contains a module for camera calibration
  • controller - stateless Controller that, upon receiving events through a TCP connection, calls the appropriate function dependent on the event's signal, returning the results of the function call to the TCP client
  • dataStorage - handles internal storage on the Jetson (e.g., saving scan information, scanned images)
  • externalComm - interface to handle external communication, particularly to the Firebase database
  • flaskServer - more appropriately named the Job Handler, but kept as "flaskServer" for legacy/lazy reasons; organizes incoming Jobs that may result from requests to the Flask Server or through a listener on Firebase; processes Jobs by sending events sequentially to the Controller to resolve each event
  • ipcProto - early prototype to test inter-process communication (between Controller and Flask Server)
  • jobs - module defining a Job, or a sequence of events that represent a task to be completed (e.g., an ImageUpload job, which consists of scanning an image with ImP and storing it internally and externally); also defines the AeroCubeEvent class -- the instance passed between the Controller and Flask Server -- and the AeroCubeSignals that dictate what action the controller takes for a given event
  • shellScripts - collection of convenient scripts (e.g., for starting up different parts of the application)
  • systemTests - collection of system tests, such as the main use case
  • tcpService - module collecting TCP logic and implementation into one location, allowing Controller and Flask Server to call it concisely

Reference

For additional information, please see our Wiki page at: https://github.com/UCSB-CS189-2016-17-Aerospace/Aerocube/wiki

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Computer vision solution to picosatellite detection and pose estimation

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