IMU's are extremely useful in SLAM.
Here is just a basic list of some of the benefits/uses.
Provides an initial guess on pose for optimization methods.
Helps filter out outliers for computer vision feature matching.
Provides scale in monocular slam.
Provides global pitch and roll estimates.
Works much better for Kalman filters prediction then a basic ...
I cannot recommend the robot_localization package in ROS enough. Please see my response to another post I made today How does sensor fusion help in robot localization. The documentation for the package is superb and I think, assuming you have ROS avaliable, you can have a EKF or UKF up and running in a week.
And as it regards the authors claim in a comment ...
I think another good place to jump right in is to examine the robot_localization package that is used in the ROS community. It implements both EKF and UKF sensor fusion estimators. The package is widely used, supported, and documented which makes it ideal for someone looking to start understanding how robot localization with multiple sensors works at a ...