Augmented Map Based Intelligent Navigation System

Abstract

The presented research work aims at augmenting maps with scene information such that it can provide intelligent capabilities. The work hence exploits existing state-of-the-art methods and evolves them to create a robust information representation framework, used for task speci c augmentation purposes. For instance, the augmented map will be able to provide a visually impaired user (or a user new to the environment for example) a scene localization information, the amount of tra c or human presence information in the scene, and guidance to destination points. The scene maps are hence augmented with localization features, tra c density information and distance maps for this purpose. In this regard, the primary computer vision tasks like map generation, scene localization, and object detection and classi cation are revisited . Substantial attention is given to suitable algorithm developments for the scene localization and object detection sub-tasks. Also, the representation framework is created in a manner such that it can be re-used for di erent sub-tasks with minimal re-computational resource requirements. Throughout this work there are two supplementary goals. While on one hand the primary task is to generate augmented maps for achieving intelligent capabilities, equal amount of attention has been given in reusing and developing the tools for facilitating higher prediction con dence, lower inference time, greater robustness. Using maps, scene recognition is an important aspect for robot navigation and localization. A trajectory based map has been generated by navigating a mobile robot. The addition of new nodes in this map is based on an user interface. This map is further populated with deep CNN features extracted from the scenes. This work particularly explores scene localization problem using state of the art deep learning models. The success of these deep networks rely on large datasets.