I'm pretty sure this is the right place for this question, but if not, please point me in the right direction!
Basically, I would like to know how do SLAM using multiple cameras and sensors that are asynchronous. Along with this, I'm curious if there's any way to use the SLAM information—or any way in general—to automatically align the pose data from multiple devices into the same coordinate system.
My specific situation is that I have devices that each have 2 cameras, 2 IMUs without built-in magnetometers, a magnetometer, and a GPS. The cameras use fisheye lenses, point different directions (but have a little overlap on their images), and capture images at the same time. However, all other sensors take measurements at different times. The IMUs have similar capture rates but don't capture at the same time, the magnetometer takes measurements much less often that the IMUs do, etc. I have the transformation matrices to convert between the sensors' local coordinate systems, along with other intrinsic and extrinsic data.
I'd like to get the orientations and positions from all of this data as well as a mapping of the environment. I also have recordings of the devices in the same location, so I want to put the devices' poses in the same reference frame so that they can be visualized at the same time. The algorithm doesn't have to do online / real-time SLAM, since I'm only analyzing the data after it's all been captured. Furthermore, it's not necessary for the algorithm to use all of the sensors, but I would like it to at least use both cameras. If possible, I'd also prefer the algorithm to not rely on ROS.
Most of the algorithms I found online didn't match my case exactly, and the one that I think might've doesn't have a public implementation that I can use.