On Saturday, May 14, 2022, we demonstrated SEDRAD at the AppWorks offices in Taipei, Taiwan. The goal was to get approval to use the robot during their upcoming Demo Day #24. The demonstration was a big success and SEDRAD is set to navigate autonomously while showing information about the participating startups in the event.
AppWorks is the leading startup accelerator in Taiwan. It helps early-stage startups with resources and advice and facilitates their access to industry experts and potential investors. AppWorks admits two batches of startups per year, and at the end of each period, they hold their awaited Demo Day. We are excited to showcase SEDRAD during the upcoming event. Stay tuned for more information.
Before you can train a supervised Deep Learning model, you must first label your data.
Today I am testing Intel OpenVINO’s CVAT and MATLAB’s Lidar Labeler annotation tools for 3D data. First impressions, CVAT makes it easier to navigate the point cloud, but a small bug makes it hard to place the initial cuboid, making things a little slower overall. Lidar Labeler’s navigation is a little more difficult, but because it has no bug when placing the cuboid, it overall becomes faster to use. CVAT being free, if they were to fix the bug, it would become the preferred tool for now.
After a few days of playing with some Makeblock (blue) metal pieces, I finally created a temporary mount for my RTK Heading (Dual GNSS) + 32-beans LiDAR system. It should be enough to test the sensors while a more stable one is built. I also conducted a quick indoor test of the LiDAR, it has been raining for two weeks so no chance to go outdoors yet.
I am pleased to announce that the first version of SEDRAD, The Self-Driving Ads Robot, is finally here. I have released it as part of the final submission of the OpenCV Spatial AI Competition #Oak2021. Watch the video to learn more about what SEDRAD is capable of doing, and if you have any questions, don’t hesitate to contact me.
An old Soul Hackers Labs’ trick now powered by Oak-D. People tracking and face detection happens on edge device. The detected face is fed to SHL’s face analyzer (on host) to determine the age, gender, emotions, attention level, viewing time of the tracked person. Metadata is produced and stored to generate useful reports for advertisers.
Tracking and detecting faces was the most resource consuming part and now the host computer has been freed from this burden, thanks Oak-D!
One of the steps of teaching the Self-Driving Ads Robot (SEDRAD) how to navigate the environment is to teach it which surfaces are good for it to drive on. We began the tedious task of collecting images of the surrounding, and creating a segmentation of the images to later “teach” SEDRAD to recognize the surface to follow and stay on. This process comprises image acquisition, image annotation (segmenting in our case), and then segmentation validation. It is very time consuming. We also ran into a problem. Due to technical issues, the real SEDRAD was not available over the weekend, when we collected the data. Instead, we mounted the cameras at the same height and separation from each other on a wagon. Annotation here we go!