I would somewhat disagree with the statement of lack of heterogenous systems. It is pretty common to always combine a camera sensor with an IMU. LIDAR + Camera has also been a pretty popular idea over the last few years.
In regards to lack of more exotic sensors e.g Thermal, RADAR I would say it is due to the fact that the main purpose of SLAM is to build 3D maps. The best sensors to give you 3D information is LIDAR and Cameras so the majority of focus has been on those two systems.
A couple of other reasons for lack of heterogeneous sensor systems:
Adding more sensors, especially more exotic ones like Thermal cameras adds to the cost of your sensor platform.
More sensors means more computational cost. Not only in the backend portion of fusing the sensor information, but also your system requires now multiple frontends. This becomes especially prevalent on small robots like UAVs, where computation is a premium.
Lack of benefits
The sensors have to complement each other. A LIDAR + Stereo Pair is not a good combination, as the purpose of the Stereo Pair is made redundant with the LIDAR.
Form Factor/Availability/ Ease of Use
The sensors could be too big to put on a small vehicle, getting access to good ones could be difficult. They may require exotic connectors, or propriety software libraries. A bunch of different reasons that make it too complicated to use compared to a simple camera.
On the topic of algorithm feasibility the backends used in Sensor Fusion such as Kalman Filters and Factor Graphs are abstract enough to allow any combination sensors. So this is not an issue.