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snapcon2022:presentation-notes

Workshop Notes

Preparation / Prerequisits

  • Download …
  • Install …
  • Print …

Introduction

  • The work of the EOLab Team → Current state of development
  • Image Classification
  • Object detection
  • Mini drones with OD

Hands On

  • Connect SNAP to the server in Nvidia Jetson
  • Image classification game
  • Object Detection ??

Reflection

Main Achievements (internal discussion)

SNAP! and Mini-Drone (Harley, 3 mins, live, with Alonzo pilot)

  • Tello SNAP Backend (Javascript backend, communication software interface, Wifi, client, binding to IP address), URL, eolab.de github
    • One drone has a default IP, it is in “station” mode (the drone is AP, AP mode), 192.168.10.1
    • Tello AP mode (client to Wifi), necessary for more than one drone in network and/or interaction with Jetson
  • Tello SNAP! category (collection of SNAP! Javascript blocks), websocket interaction with the interface talking to the drone

Jetbot and Object Detection with SNAP! (Ali, 3 mins, with Alonzo driver)

  • Object follower
  • Jetbot Camera
  • SNAP! is running remotely, could be running on Jetbot
  • DetectNet (SSD-MobileNet V2, CoCo Dataset, 91 Classes)
  • Closed Loop Control
  • TODO: Short video!

Object Detection: Follow an Object with Drone (Ilgar, 3 mins, with Alonzo pilot)

  • Based on Harley's presentation on Tello SNAP! interaction
  • New aspect: Object detection, Jetson
  • Challenges
    • Video stream from Tello drone to SNAP! (25 fps)
    • Video stream from SNAP! to Jetson (extacting stage in base64 format, send message, wait response, sequential, 7 fps)
    • Receive response from Jetson to SNAP! (bounding box, class label, coordinate transformation to the stage)
  • Frame rate incl. analysis is 7 fps
  • Problem (not serious): Realtime delay (latency) within the Tello drone video stream!
  • TODO: Short video!
snapcon2022/presentation-notes.txt · Last modified: 2023/01/05 14:38 by 127.0.0.1