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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

SLAM based techniques are often adopted for solving the navigation problemfor the drones in GPS denied environment. Despite the widespread success ofthese approaches, they have not yet been fully exploited for automation in awarehouse system due to expensive sensors and setup requirements. This paperfocuses on the use of low-cost monocular camera-equipped drones for performingwarehouse management tasks like inventory scanning and position update. Themethods introduced are at par with the existing state of warehouse environmentpresent today, that is, the existence of a grid network for the groundvehicles, hence eliminating any additional infrastructure requirement for dronedeployment. As we lack scale information, that in itself forbids us to use any3D techniques, we focus more towards optimizing standard image processingalgorithms like the thick line detection and further developing it into a fastand robust grid localization framework. In this paper, we show different linedetection algorithms, their significance in grid localization and theirlimitations. We further extend our proposed implementation towards a real-timenavigation stack for an actual warehouse inspection case scenario. Our linedetection method using skeletonization and centroid strategy works considerablyeven with varying light conditions, line thicknesses, colors, orientations, andpartial occlusions. A simple yet effective Kalman Filter has been used forsmoothening the {\rho} and {\theta} outputs of the two different line detectionmethods for better drone control while grid following. A generic strategy thathandles the navigation of the drone on a grid for completion of the allottedtask is also developed. Based on the simulation and real-life experiments, thefinal developments on the drone localization and navigation in a structuredenvironment are discussed.

Grid-based Localization Stack for Inspection Drones towards Automation of Large Scale Warehouse Systems