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Hi @reavers92
As I can see, you are only using wheel odometry to localize the robot. This is a good start but you will need more odometry sources to increase the precision of your localization. Since naturally, wheel odometry will end up having too much error due to several things like, wheel sliding, mechanicar issues, bad approximation in the computations etc. I will recommend you to check the robot_localization package which include EFK, UFK nodes ables to produce precisse localization from filtering with a Kalman Filter several odometry sources (GPS, IMU, Wheel_odometry, etc.).
Furthermore, since you have a LiDAR and, depending on your environment, you can localize yourself pretty well with the AMCL approach, a set of nodes that will perform a comparison between the LiDAR readings and an offline map to localize the platform within the map. Besides, this odometry is suitable also to be used with robot_localization together with your wheel odometry.
Antoher good package can be LOAM that is basically "Laser Odometry and Mapping [...] a realtime method for state estimation and mapping using a 3D lidar".
I am sure there are more solutions out there, I just wrote what I consider the most important ones. If anyone know more or better approaches I will glad to hear them.
Hope this helps.
Regards.
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