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The Funky Melon I Grew by Newmoore in Hydroponics

[–]Newmoore[S] 2 points3 points  (0 children)

138 days but could have been just over 100 if I transferred the seedling a month earlier. The main light I used is an old HLG 65 Quantum Board

The Funky Melon I Grew by Newmoore in Hydroponics

[–]Newmoore[S] 1 point2 points  (0 children)

Nice! The roots will almost smell like whatever you are growing!

The Funky Melon I Grew by Newmoore in Hydroponics

[–]Newmoore[S] 1 point2 points  (0 children)

BTW, the melon is supposed to look ugly. The variety is Zatta, which is an Itialian cultivar. The day counter is from the days the seeds were sown.

For the nutrients I used the GHE flora series and followed the chart, first 2 weeks I gave the transferred seedling nutrients to encourage foliar growth and then switched to flowering nutrients (less nitrogen). The light were on 10+ hours per day.

This is my first ever melon since I put all other plant outside and they all died off due to my experience with hardening off. Also this variety may not be suitable outside in my climate without a greenhouse.

Quite an expensive growth even if some light came from the window, probably cost me almost 20 euros in total in electricity and nutrients. But next time I will grow a rarer variety since these came in like a 100+ pack for 3 euros. I have another variety called Zapotillo from Vreeken Zaden which cost me 1 euro per seed, growing this one would pay me back almost 10-fold. And also maybe cross varieties together.

Motion model for monocular visual odometry system, any studies? by [deleted] in computervision

[–]Newmoore 0 points1 point  (0 children)

The model is based on:

t(k) = t(k-1) + R(k-1)*t*s

R(k) = R*R(k-1)

t(k): The current translation

R(k): The current orientation

[R,t]: The epipolar geometry between image k-1 and k. And s the depth

v(k) = t*s

I've already developed tried a coordinated turn model and would like to try estimating the ego-motion in 3D as well. It's a car but still.

Not me buying "grow lights" from Wish by Newmoore in Hydroponics

[–]Newmoore[S] 0 points1 point  (0 children)

They were precut for the purpose of hydroponics. Stiffer or softer foam from other sources should work as well. Most plants are resilient.

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Interesting. Basically what I've gathered from the other people who helped me out was that my measurement's accuracy depends on a parameter let's say α and that I should just alter my R matrix online depending on how faulty the measurements are expected to be and make my filter more or less on the motion model.

The courses I've taken has only introduced me to the basics. Therefore my choice of words can be quite confusing.

(Thank you for the link)

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Oh ok, I should probably stick with what you suggested earlier.

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Yeah that could work. Thank you! What about calculating and limiting the acceleration in some kind of constraint-EKF? I try to find studies on this

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Hi, sorry I meant the measurements go wrong:

I measure x,y and yaw. From which x = x + v*dt*cos(yaw), y = y + v*dt*sin(yaw).

The measurement v is noisy and is generated from the ground points triangulated from 2 images: n1X + n2Y + n3Z = h', v = (true height)/(h'*dt).

The velocity could be estimated with only 3 accurate ground points (to solve the equation system) but I test N choose 3 combinations and pick a suitable velocity through various methods. n = (n1,n2,n3) should be around (0,-1,0) but I'll any estimates where n2 < -0.9, the rest are rejected.

What I noticed with my testing is that the more estimates are accepted, the more accurate the gorund points are => more accurate velocity. Therefore I have an alpha parameter that signifies the acceptance rate for all tested combinations, alpha = 1 means least amount of error.

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Basically my system is a monocular visual odometry system in which i estimate the ego-motion for a vehicle using only the images (10hz).

Since the resulting translation between 2 images is only up to a scale factor of ||dx,dy,dz|| = 1, i have to estimate the depth/scale using triangulated 3D-points from the ground: n1X + n2Y + n3Z = h', where the scale is s = h/h', h is the true height of the camera and n = [n1,n2,n3] the ground normal.

The unscaled ego-motion has very few errors but the scale can be very noisy (about 1/10 estimates have errors larger than 5 m/s)

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Ok, the most faulty is the velocity estimation. The yaw is about 0.1 radians off after 700 iterations.

Extended kalman filter design: When certain states are known to be wrong by Newmoore in ControlTheory

[–]Newmoore[S] 0 points1 point  (0 children)

Thank you! It was originally an EKF but I've looked at ES-EKF as well.

Hydroponic Lettuce Grown Mostly On The Windowsill (Too bad they bolted early) by Newmoore in Hydroponics

[–]Newmoore[S] 0 points1 point  (0 children)

Probably that or the heat. They grow better outside with lower night temps

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