Category: Thoughts

“`
sudo apt-get update
sudo apt-get install -y software-properties-common
sudo add-apt-repository ppa:certbot/certbot
sudo apt-get update
sudo apt-get install -y python-certbot-nginx
“`
Then get a certificate `sudo certbot –nginx certonly`

Now edit the nginx config to add the fullchain.pem and privkey.pem paths.

sudo vim /etc/nginx/sites-enabled/default

Add lines 22-25.

server {
20 # SSL configuration
21 #
22 listen 443 ssl default_server;
23 listen [::]:443 ssl default_server;
24 ssl_certificate_key /etc/letsencrypt/live/autox.network/privkey.pem;
25 ssl_certificate /etc/letsencrypt/live/autox.network/fullchain.pem;
…
}

“In mathematics, the four colour theorem, or the four colour map theorem, states that, given any separation of a plane into contiguous regions, producing a figure called a map, no more than four colour are required to colour the regions of the map so that no two adjacent regions have the same colour.” — Wikipedia

“A codebase composed of small interconnected modules that communicate with each other in a well-defined way will have a lower complexity than one where the modules communicate with each other haphazardly.” – http://alexkudlick.com/blog/what-the-four-color-theorem-can-teach-us-about-writing-software/

One time I accidentally messed with the `/etc/passwd` and locked myself out of being able to SSH into the machine. Since this is a remote machine in AWS I had no way of doing what I’d normally do. Which is attaching a keyboard and monitor and fixing this manually.

To fix, use the AWS EC2 Management page to:
– spin up a new instance of vanilla ubuntu EC2 (let’s call it David)
– shutdown the locked machine (let’s call it Goliath)
– unmount Goliath’s volume
– attach the volume to David

Then follow this guide: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ebs-using-volumes.html

Summary of what I did from this guide:
“`
lsblk
sudo file -s /dev/xvdf # MBR (not data type)
sudo file -s /dev/xvdf1 # ext4
sudo mkdir mount_folder
sudo mount /dev/xvdf1 mount_folder # ext4 mounted
cd mount_folder
# undo crazy setting (see Note #1)
cd .. # to unmount
sudo umount /dev/xvdf1
# Note #2
“`

Note #1: For me I tried to modify `/etc/ssh/sshd_config` to allow one more user to login. But this made me unable to login after. So I removed the offending line.

Note #2: now in the Volume webpage
– undo attach to David (Volumes tab)
– mount to Goliath (Volumes tab: attach as EBS path /dev/sda1)
– boot up Goliath (Instance tab)

 

Done/No,Description,Amount
TRUE,Cooler Master MasterLiquid Lite 240mm Liquid Cooling,$42.89
TRUE,Intel i7 7700 CPU,$269.36
TRUE,ASRock Z270 KILLER SLI/AC Z270 Motherboard,$128.69
FALSE,Mobo MIR,-$30.00
TRUE,”EVGA SuperNOVA 1000 G2, 80+ GOLD 1000W, Fully Modular”,$118.51
TRUE,Thermaltake Core G21 Dual 4mm Tempered Two-Toned Glass Power Cover ATX Black Gaming Computer Case ,$64.34
FALSE,Case MIR,-$20.00
TRUE,Crucial Technology 32GB (2x 16GB),$259.99
TRUE,ZOTAC GeForce GTX 1060 6GB Mini,$267.89
TRUE,Toshiba Solid State Drive PCIe NVMe M.2 512GB,$247.90
TRUE,Windows 10 Home,$92.99
TRUE,Oculus Rift Headset,$679.99
TRUE,Oculus Touch Controller,$99.99
FALSE,,
,Total,”$2,272.54″

SLAM is simultaneous localization and mapping; the goal is to build/update a map while simultaneously keeping track of location within.

In other words, SLAM takes sensory data as input (such as camera, lidar, ultrasound) and outputs a partial map and location within.

A popular open source framework is called ORB SLAM. This is fast, robust, and efficient.

 

I’ll summarize my findings here.

• https://github.com/raulmur/ORB_SLAM2
• https://github.com/dorian3d/DBoW2

There are three papers you can read that describes how it works. You should read them in order, because the ideas build on each other (1-2) and (3) describe improvements made.

1. Bag of Binary Words
2. ORB-SLAM paper
3. ORB-SLAM2 paper

I’m not much of an expert in this field, so I’ll try to explain in layman’s terms of how I understand it. Here are essence of what you need to know.

• Sensory data are a stream, but it can be discretized, such as a video stream.
  • Discrete means to represent a real space using a discrete quantities.
• Videos are sequences of images.
• In an image, there are points that can be easily identified and tracked across images.
• These points can be identified by an algorithm called Bag of Words (read 1).
  • A picture is like a paragraph, it can be described by visual words.
  • Visual words are small images that represent like noses, eyes, mouth.
  • With enough words, you can guess what objects you’re looking at, and from what angle.
  • For example, if you had two eyes and a nose, you would guess a face.
  • And if the two eyes are much bigger than the nose, you’re likely looking downward angle to the face.
• Bag of Binary Words improve performances of the Bag of Words (BoW) model.
• The ORB-SLAM system uses the BoW model to build a database.
  • Database stores BoW features that represent an image.
    • Meaning the image was converted into BoW features.
  • These features are used as an index into a location map.
• From an image and a location, a Key Frame can be created.
  • Key Frames are an association container, which can represent a mapping of BoW features into a frame in location space, and vice versa.
• Using these BoW features, and sensor calibration data, a location is triangulated from mathematical calculation.
• Many of these Key Frames are linked together.
  • You can think of Key Frames being a vertex in a graph, and an edge represent co-visibility.
    • That is, the BoW feature is visible from the other Key Frame as well.
  • This graph is called a covisibility graph.
  • Which represents a map.
• Having this graph aids in knowing when to insert a new Key Frame into the database.
  • For example, when two Key Frames have less than 75% common BoW features.
• When given an image, the database is asked to return a list of plausible locations.
  • The image is converted into BoW feature.
  • This feature is used to search the covisibility graph to find similar Key Frames.
  • Returns a list of Key Frames with statistical likelihood to represent which Key Frames contains the most matched BoW features.
  • The covisibility graph reduces search complexity, therefore speeding up search, and also reduces storage needs.
• So now a map is being built at the same time it is localized.
  • SLAM.
• In the future, we can come back with more data to:
  • Improve the map.
  • Ask the database for a localization result.

That’s all folks. I’ll add some pictures later.

Here’s a video.