Abstract: This disclosure relates to systems and methods for performing inclusive indoor navigation. State of the art systems and methods require extra hardware and fail to provide accurate localization and navigation with less precision. The method of the present disclosure obtains a nested environment data of a facility and estimate current spatial location of a user in the nested environment using surrounding recognition machine learning model. An optimal path categorized as convenient path, shortest path and multi-destination path from the current spatial location to a destination is determined. The current spatial location of the user is tracked on the optimal path using an augmented reality technique when navigation starts. The optimal path is dynamically updated based on feedback obtained from one or more user interaction modalities. The present disclosure provides user navigation with last meter precision, and no hardware and internet dependency.

Abstract: This disclosure relates to systems and methods for performing inclusive indoor navigation. State of the art systems and methods require extra hardware and fail to provide accurate localization and navigation with less precision. The method of the present disclosure obtains a nested environment data of a facility and estimate current spatial location of a user in the nested environment using surrounding recognition machine learning model. An optimal path categorized as convenient path, shortest path and multi-destination path from the current spatial location to a destination is determined. The current spatial location of the user is tracked on the optimal path using an augmented reality technique when navigation starts. The optimal path is dynamically updated based on feedback obtained from one or more user interaction modalities. The present disclosure provides user navigation with last meter precision, and no hardware and internet dependency.