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

Simultaneous localization and mapping (SLAM) in slowly varying scenes isimportant for long-term robot task completion. Failing to detect scene changesmay lead to inaccurate maps and, ultimately, lost robots. Classical SLAMalgorithms assume static scenes, and recent works take dynamics into account,but require scene changes to be observed in consecutive frames. Semi-staticscenes, wherein objects appear, disappear, or move slowly over time, are oftenoverlooked, yet are critical for long-term operation. We propose anobject-aware, factor-graph SLAM framework that tracks and reconstructssemi-static object-level changes. Our novel variationalexpectation-maximization strategy is used to optimize factor graphs involving aGaussian-Uniform bimodal measurement likelihood for potentially-changingobjects. We evaluate our approach alongside the state-of-the-art SLAM solutionsin simulation and on our novel real-world SLAM dataset captured in a warehouseover four months. Our method improves the robustness of localization in thepresence of semi-static changes, providing object-level reasoning about thescene.

POV-SLAM: Probabilistic Object-Aware Variational SLAM in Semi-Static Environments