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

This paper concerns simultaneous localization and mapping (SLAM) of large areas. In SLAM the map creation is based on identified landmarks in the environment. When mapping large areas a vast number of landmarks have to be treated, which usually is very time consuming. A common way to reduce the computational complexity is to divide the visited area into submaps, each with a limited number of landmarks. This paper presents a novel method for merging conditionally independent submaps (generated using e.g. EKF-SLAM) by the use of smoothing. By this approach it is possible to build large maps in close to linear time. The approach is demonstrated in two indoor scenarios, where data was collected with a trolley-mounted stereo vision camera.

Smoothing-Based Submap Merging in Large Area SLAM