This paper introduces the open-source framework, GIRA, which implements fundamental robotics algorithms for reconstruction, pose estimation, and occupancy modeling using compact generative models. Compactness enables perception in the large by ensuring that the perceptual models can be communicated through low-bandwidth channels during large-scale mobile robot deployments. The generative property enables perception in the small by providing high-resolution reconstruction capability. These properties address perception needs for diverse robotic applications, including multi-robot exploration and dexterous manipulation. State-of-the-art perception systems construct perceptual models via multiple disparate pipelines that reuse the same underlying sensor data, which leads to increased computation, redundancy, and complexity. GIRA bridges this gap by providing a unified perceptual modeling framework using Gaussian mixture models (GMMs) as well as a novel systems contribution, which consists of GPU-accelerated functions to learn GMMs 10-100x faster compared to existing CPU implementations. Because few GMM-based frameworks are open-sourced, this work seeks to accelerate innovation and broaden adoption of these techniques.

翻译：本文介绍开源框架GIRA，该框架利用紧凑生成式模型实现重建、位姿估计与占用建模等基础机器人算法。紧凑性确保在大规模移动机器人部署过程中，感知模型可通过低带宽信道进行通信，从而实现大尺度感知；生成特性则通过高分辨率重建能力实现小尺度感知。这些特性可满足多机器人探索与灵巧操作等多样化机器人应用的感知需求。现有先进感知系统通过复用相同底层传感器数据的多个异构流水线构建感知模型，导致计算冗余与复杂度增加。GIRA通过提供基于高斯混合模型（GMM）的统一感知建模框架弥合这一差距，其创新性系统贡献包括GPU加速函数，相较于现有CPU实现可将GMM学习速度提升10-100倍。鉴于当前开源GMM框架极少，本研究旨在加速相关技术革新并推广其应用。