There are a variety of Simultaneous Localization and Mapping algorithms, that are used in navigation, or Augmented reality systems, almost all of which use probabilistic and statistical analysis to perform the computation which allows a machine to have a state of situational awareness. Historically it is known that most of SLAM variations are computationally heavy. Therefore, real time implementations with limited battery requirements such as the ones used in wearable and headsets have faced challenges to become reality. 

However, recent advancements in the field of SLAM, such as QSLAM have introduced new concepts to the fast-growing field of robotics. 

This article from Brown University compares qSLAM for Real-Time Place Recognition by Safa at al with the work Gupta et al presented for an autonomous learning framework. It also takes a look at SLAM and Recent advancements the field of Simultaneous Localization and Mapping and the work that is taking place by industry. 

With its lightweight nature, QSLAM enables the robot to consider a continuum of possible movements or the phase-space of the robot which is closer to how humans perceive the world. It further enables Collaborative AI Technology where different devices work together to solve common problems. 

While older technologies such as pure LIDAR systems, or others such as VI SLAM, VIO SLAM, VSLAM will still be around for the next few years, newer methods of mathematical modeling are challenging the status-que and will make their way into the market soon enough.