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This is exactly what I am researching right now. I am working on a project build a robot that maps its surroundings and walks around. The goal is to learn some

This is exactly what I am researching right now. I am working on a project build a robot that maps its surroundings and walks around. The goal is to learn some AI (slam and camera based ML) and also hardware (Robot itself). I am thinking of raspberry pi with cameras and then using just building the slam algo from scratch. Is this a good option or am I trying to bite off more than I can chew. I understand ML theory but haven't done any other than the basic 101 ML programs and the coursera course. I am fluent in OS (systems), C, python. Can folks with knowledge opine on this? Are there better ways to learn ML? I chose the robot so I can have something to keep on building/adding

aktenlage 2y ago
Depends on what you want to learn. The SLAM Frontend (computing motion information from sensors) offers a lot of variety through the chosen combination of sensors (wheel odometer, IMU, mono/stereo/multi camera, lidar, radar, sonar, to name a few). At least for vision, deep learning should be very useful. For the others I have no experience how much machine learning is relevant. Geometry and physics based methods should work well here, but there is probably much room to tack on some ML. The SLAM backend (optimization) is mostly old-school methods like nonlinear least squares optimization or particle filters. Not sure if that counts as ML today. I'd go for g2o or Ceres for mapping (unless you expect to have no loop closures), as there's really no need to reinvent that. It's definitely useful to learn about the backend, but usually the combination of sensors and their properties will demand more algorithmic tailoring than the backend, which gets more abstract input (i e. motions & uncertainties) and can be used more black-boxy.
- yalogin 2y ago
  Thanks for the input, would you recommend developing slam from scratch or start with some libraries and tailor it to my sensors?

aktenlage 2y ago

Depends on what you want to learn. The SLAM Frontend (computing motion information from sensors) offers a lot of variety through the chosen combination of sensors (wheel odometer, IMU, mono/stereo/multi camera, lidar, radar, sonar, to name a few). At least for vision, deep learning should be very useful. For the others I have no experience how much machine learning is relevant. Geometry and physics based methods should work well here, but there is probably much room to tack on some ML. The SLAM backend (optimization) is mostly old-school methods like nonlinear least squares optimization or particle filters. Not sure if that counts as ML today. I'd go for g2o or Ceres for mapping (unless you expect to have no loop closures), as there's really no need to reinvent that. It's definitely useful to learn about the backend, but usually the combination of sensors and their properties will demand more algorithmic tailoring than the backend, which gets more abstract input (i e. motions & uncertainties) and can be used more black-boxy.

yalogin 2y ago
Thanks for the input, would you recommend developing slam from scratch or start with some libraries and tailor it to my sensors?