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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Reliability of SLAM systems is considered one of the critical requirements inmodern autonomous systems. This directed the efforts to developing manystate-of-the-art systems, creating challenging datasets, and introducingrigorous metrics to measure SLAM performance. However, the link betweendatasets and performance in the robustness/resilience context has rarely beenexplored. In order to fill this void, characterization of the operatingconditions of SLAM systems is essential in order to provide an environment forquantitative measurement of robustness and resilience. In this paper, we arguethat for proper evaluation of SLAM performance, the characterization of SLAMdatasets serves as a critical first step. The study starts by reviewingprevious efforts for quantitative characterization of SLAM datasets. Then, theproblem of perturbation characterization is discussed and the linkage to SLAMrobustness/resilience is established. After that, we propose a novel, genericand extendable framework for quantitative analysis and comparison of SLAMdatasets. Additionally, a description of different characterization parametersis provided. Finally, we demonstrate the application of our framework bypresenting the characterization results of three SLAM datasets: KITTI,EuroC-MAV, and TUM-VI highlighting the level of insights achieved by theproposed framework.

Are We Ready for Robust and Resilient SLAM? A Framework For Quantitative Characterization of SLAM Datasets