English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Autonomous navigation is the future of robotics and object recognition is one of the vital activities that need to be performed by robots. Autonomous robots are used in various applications namely household cleaning, Goods delivery, factory automation and safety measures [1]. Autonomous navigation is a complex task that involves localization, mapping and path planning. Localization is the basic step in autonomous navigation since the robot has to know or identify the position of itself in an environment. Localization is performed by triangulation method. Mapping involves associating the scanned data set obtained from each environment to derive overview topography of the scenario [2]. LIDAR (Light Imaging Detection and Ranging) is used in the developed robot to detect all fine objects present in the indoor environment precisely, based on which the three dimensional (3D) map of the environment is constructed. The 3-D LIDAR consist of a transmitting laser and a receiver. LIDAR emits laser signals in 360 degrees, whenever this signal hits an obstacles, it gets reflected and received by the receiver present in LIDAR. From the reflected signal, the meta data namely position, height and distance of the object is computed. In simple terms LIDAR is a scanner that could create an exact digital copy of all the physical objects present in the considered environment with precise measurements. The mobile object identifier first builds a virtual map in which it could travel by combining various LIDAR scan results of the environment. This robot uses the SLAM algorithm for localization and mapping activities, the PRM is used to create the possible navigation path for the robot from source to destination. The mobile object identifier identifies the object’s material with computer vision and machine learning in its route to the destination. The contents of the paper are organized as following. Section II describes the literature of related works. Section III defines the proposed work. Section IV details the experimental outputs. Finally, the conclusion of this work is summarized in Section V.

SLAM based Autonomous Navigating Object Identifier for an Indoor Environment using 3D - LiDAR and MoBiNet