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

This paper introduces the open-source framework, GIRA, which implementsfundamental robotics algorithms for reconstruction, pose estimation, andoccupancy modeling using compact generative models. Compactness enablesperception in the large by ensuring that the perceptual models can becommunicated through low-bandwidth channels during large-scale mobile robotdeployments. The generative property enables perception in the small byproviding high-resolution reconstruction capability. These properties addressperception needs for diverse robotic applications, including multi-robotexploration and dexterous manipulation. State-of-the-art perception systemsconstruct perceptual models via multiple disparate pipelines that reuse thesame underlying sensor data, which leads to increased computation, redundancy,and complexity. GIRA bridges this gap by providing a unified perceptualmodeling framework using Gaussian mixture models (GMMs) as well as a novelsystems contribution, which consists of GPU-accelerated functions to learn GMMs10-100x faster compared to existing CPU implementations. Because few GMM-basedframeworks are open-sourced, this work seeks to accelerate innovation andbroaden adoption of these techniques.

GIRA: Gaussian Mixture Models for Inference and Robot Autonomy