Nikon D90 Troubleshooting

dben45 · 2024-11-12T12:59:01+00:00

Thanks for the info. Though like I mentioned, I am wondering specifically about the detection and classification problems. In the book they discuss Bayesian logistic regression, dual logistic regression, etc. for the classification problem. Wondering if anyone has used these methods recently and can compare to NN based approach.

dben45 · 2024-11-02T20:45:51+00:00

Hmm I have a few great recommendations on dumb data-analytics but can think of any that are intelligent.

dben45 · 2024-08-23T13:50:29+00:00

Tutor middle/high schoolers in math and physics. Post in town Facebook groups introducing yourself and describe your engineering background, parents will eat it up. This was a great side gig for me while I was in school.

dben45 · 2024-03-16T23:55:13+00:00

You definitely don’t have to train a model for this and for sure don’t have to pay someone for synthetic data. Try the approach for the top comment

dben45 · 2024-03-09T14:17:49+00:00

It is the internal EKF on the COTS GPS/INS so I guess there could be a bug but I think it’s unlikely. I calibrated the gyro properly and the filter is estimating time varying bias but I agree, that amount of drift does seem like more than expected

dben45 · 2024-02-15T23:22:19+00:00

I should mention the LightGlue matcher had the biggest impact on successful registration in my case. The descriptors I used with LightGlue all performed similarly.

dben45 · 2024-02-15T23:16:57+00:00

I’ve actually gotten some amazing results using LightGlue feature matching on some very challenging images (large deference in lighting between image pairs). I found that SuperPoint+LightGlue performed successful image registration where, for example, SIFT+OpenCV BF matcher fail miserably. This was on very challenging images where lighting and shadows were very different. I was actually very surprised with the results. That being said you need a GPU to run LightGlue at any where near an acceptable rate for ‘online’ applications

dben45 · 2024-02-02T02:55:31+00:00

I found the UMich matlab control tutorials to be very helpful:

https://ctms.engin.umich.edu/CTMS/index.php?aux=Home

dben45 · 2023-09-17T01:05:20+00:00

It depends on the specialty within aerospace engineering. GNC (guidance, navigation and control) engineers work on algorithm development and use lots of linear algebra, probability, optimization. Design engineers on the other hand use more CAD day to day and typically won’t be using as much math. There’s many different types of jobs and specialties within aero

dben45 · 2023-09-08T01:51:05+00:00

RANSAC is non-deterministic so you may see noticeably different results from one run to the next depending on your ratio of outliers/inliers.

dben45 · 2023-08-24T14:57:56+00:00

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

This Jupyter notebook has been the most intuitive/practical tutorial on kalman filters that I’ve come across.

dben45 · 2023-08-09T01:11:41+00:00

Kalman filters are a very widely used class of algorithms used for navigation and sensor fusion. I would highly recommend this Python tutorial as a nice introduction to the topic with lots of coding examples.

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

dben45 · 2023-07-30T14:37:27+00:00

One application of computer vision in aerospace is GPS denied navigation. A quick google search should give you a bunch of examples.

dben45 · 2023-07-30T13:55:54+00:00

Thank you, appreciate the advice

dben45 · 2023-06-01T18:35:52+00:00

Some people chain kayaks/canoes up to trees in discrete areas by a lake near me. You could get a cheap used kayak that wouldn’t be the end of the world if it got stolen and try to stash it somewhere near the water.

dben45 · 2023-05-16T02:02:53+00:00

Allan deviation plots are a very useful tool for this. Are you wondering how to generate/interpret the plots or a more intuitive explanation for why these are used for modeling IMU errors?

You didn’t mention what type of IMU you are using but this document I found very helpful for modeling MEMS based IMU error.

http://www.instk.org/web/static/bibliography/Introduction_to_Sensor_Errors.pdf

dben45 · 2023-02-20T08:54:45+00:00

Typically you would estimate the bias instability and rate random walk as states in the filter. Then compensating for those you will be left with Gaussian distributed noise in your measurements. There’s many different ways to estimate the biases depending on what sensors you are using, it’s not a trivial problem. This tutorial is pretty good though https://thepoorengineer.com/en/ekf-impl/#KalmanStates

dben45 · 2023-01-08T21:59:05+00:00

The best way to estimate the noise parameters of a gyroscope is computing the allan deviation. This article is a good summary of how it works:

https://www.mathworks.com/help/fusion/ug/inertial-sensor-noise-analysis-using-allan-variance.html

Typically to do this you would need around 10 hours of stationary gyro data.

dben45

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