Output xyz, identification name to console

Hi Brandon,

Thank you for your quick reply! I will look into it this weekend.

My story is a long one so I will make a short one.

a few years ago I built a robotic lawn mower, which does not use a boundary wire. Instead I used real-time kinematics, which gives me a position around 2 cm and 100% localization.
This opens a lot of doors...
First, as you may or may not know a robotic lawn mower cuts your lawn in a random pattern, meaning it never knows when your lawn is finished, so it cuts and cuts and cuts, eventually everything is cut and all looks fine.

However, with 100% localization, you can now cut your lawn in a systematic pattern. This means for my 500 sqm lawn, its completely done in 2 hours. And I can look at my phone how much is left etc... In an event of power failure, unwanted weather condition, it will save the position and head home. When everything is good again it will go back to the position and continue the program.

So, last summer I made a small video on my phone, put it on linkedin, then it exploded. I got around 600K views, and companies started to approach me. In the end I created a company, signed a deal with a robotic lawn mower company.

So, why do I need a camera? Well, in random pattern you bump into something and go backwards, turn around and go in a another direction. Simple and it works.
In a systematic program, you can´t do that. You have a pattern to follow, and if you bump into something you need to find a way around the obstacle, this is where the camera comes in, to identify the object, how big is it? what side can I go around it etc....
So right now we have two possibilities, use a camera or use a radar. Radar is easier, costs less etc.. but a camera can do so much more, so I need to explore this possibility.

Drop me an email at: calle at lanstep dot com if you want to see it in action.

Thanks, and I´m sure I will get back to you with more questions
//Carl

Edit: oh, no ROS is getting into my robots, I write everything myself for total control.

ok, I´m getting close!
I have this minimum code, it will give me what I want, BUT I´m missing the depth information:

`from pathlib import Path

import cv2
import depthai
import time

class DepthAI:
def init(self):
self.device = depthai.Device('', False)
self.p = self.device.create_pipeline(config={
"streams": ["metaout", "depth"],
"ai": {
#blob compiled for maximum 12 shaves
#blob can be generated using: python3 depthai_demo.py -cnn mobilenet-ssd -sh 12
#it will be written to <path_to_depthai>/resources/nn/mobilenet-ssd/mobilenet-ssd.blob.sh12cmx12NCE1
"blob_file": "/home/pi/depthai/resources/nn/mobilenet-ssd/mobilenet-ssd.blob.sh12cmx12NCE1",
"blob_file_config": "/home/pi/depthai/resources/nn/mobilenet-ssd/mobilenet-ssd.json",
"shaves" : 12,
"cmx_slices" : 12,
"NN_engines" : 1,
},
})

  self.detections = []

def run(self):
    while True:
        nnet_packets, data_packets = self.p.get_available_nnet_and_data_packets()
        for nnet_packet in nnet_packets:
            self.detections = list(nnet_packet.getDetectedObjects())
            for detection in self.detections:
             print (detection.get_dict())

    del self.p
    del self.device

DepthAI().run()
`
The output:

What am I missing?

Thanks,
Carl

First real outdoor test with my test robot:

OneDrive link, about 3 min long:

https://1drv.ms/v/s!Apv2S-u3rGa7jcYNdeS0GjTQXSq8kQ

This a custom pcb board I designed which runs on a ARM mcu, and one RPi that snaps on to the board.
In this video Im using the disparity stream which detects objects in three ROI, left, front and right.
(Sorry about the Swedish words in the video, I presented this to a group of R&D people.)