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 is becoming a key component of robotics and augmented reality (AR)systems. While a large number of SLAM algorithms have been presented, there hasbeen little effort to unify the interface of such algorithms, or to perform aholistic comparison of their capabilities. This is a problem since differentSLAM applications can have different functional and non-functionalrequirements. For example, a mobile phonebased AR application has a tightenergy budget, while a UAV navigation system usually requires high accuracy.SLAMBench2 is a benchmarking framework to evaluate existing and future SLAMsystems, both open and close source, over an extensible list of datasets, whileusing a comparable and clearly specified list of performance metrics. A widevariety of existing SLAM algorithms and datasets is supported, e.g.ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones isstraightforward and clearly specified by the framework. SLAMBench2 is apublicly-available software framework which represents a starting point forquantitative, comparable and validatable experimental research to investigatetrade-offs across SLAM systems.

SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM