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

The monocular vision-based simultaneous localization and mapping (vSLAM) isone of the most challenging problem in mobile robotics and computer vision. Inthis work we study the post-processing techniques applied to sparse 3Dpoint-cloud maps, obtained by feature-based vSLAM algorithms. Mappost-processing is split into 2 major steps: 1) noise and outlier removal and2) upsampling. We evaluate different combinations of known algorithms foroutlier removing and upsampling on datasets of real indoor and outdoorenvironments and identify the most promising combination. We further use it toconvert a point-cloud map, obtained by the real UAV performing indoor flight to3D voxel grid (octo-map) potentially suitable for path planning.

Sparse 3D Point-cloud Map Upsampling and Noise Removal as a vSLAM Post-processing Step: Experimental Evaluation