[quadrature]

Slightly offtopic but i feel like someone around here might be able to help. I've been learning how to do SLAM with LIDAR data and I was curious what algorithms robot vacuums use. I'm currently implementing a particle filter and will also try out EKF.

[martythemaniak]

I've been playing around with SLAM using a depth camera, so I can't really tell you about LIDAR specifically, but I'd suggest doing some Deep Researches on the topic to get you a good lay of the land. During my searches for example, I came across this great compilation of visual SLAM projects: https://github.com/VSLAM-LAB/VSLAM-LAB

Unless you're a massive operation, you're probably just using an existing academic project, many of which handle a variety of inputs (depth, 2D lidar, 3D lidar etc), ie RTABMAP (what I started with), ORB-SLAM, nVidia Issac ROS SLAM (if you're on Jetson) etc. AMCL is an old-school algorithm for localization with 2D data - I tried it by taking a fake 2D scan from the depth camera and it was pretty terrible, so currently I'm trying to get visual-only SLAM working well enough for me because I don't want to spend $1k on a decent 3D lidar.

[quadrature]

> but I'd suggest doing some Deep Researches on the topic to get you a good lay of the land

thanks for the resources !. I've been trying to get a wide view by looking at different algorithms, but I was curious what was actually used in production systems especially for consumer products.

RTABMAP and Cartographer came up in my searches, will definitely give these a closer look to understand how they work.

Right now im starting off with filter based approaches like Particle filter and Kalman filter, but i'd also like to understand how the graph based approaches work.