Familiarity is one factor, but there are others.

https://en.m.wikipedia.org/wiki/Monocular_vision#Monocular_c...

I was looking into mobile SLAM use cases a while back and my theory around why google is pushing tango instead of simple optimizing for current hardware is they have the capital to develop for an infinitly long time horizon. It's a low risk assumption to say eventually all phones will have tango-class sensors and this way google can be the leader when the time comes.

If you've got typical levels of binocular depth perception:

Close one eye. Slowly poke something 2-3 feet away. You can do it, but you might be off an inch or two with your distance estimation and be surprised when you actually make contact. Open both eyes and do something similar with another object. The added depth information should mean that you can tell where something is to a greater precision.

Same thing with Tango. In both cases, you can extract some information about the environment from a 2D image stream. Add in accelerometer, gyroscope, and depth-sensing hardware, and you can correct some of the edge cases while increasing precision even for the things that you could do with standard hardware.

Why google keeps pushing Tango is an excellent question - there are some good people in the group, but in general the performance and achievements have been abysmal. Considering that TensorFlow and Tango come from the same organization would produce serious cognitive difference if teh googz scale wasn't taken into account - yes, I worked with Tango for a little over a year, and then gave it to my son as a game platform - seems the future is heading away from structured light to pure visual mechanisms, possibly with LIDAR or ToF for a little analytical support