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：摘要：本文介绍了开源框架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。紧凑性通过确保感知模型能在大规模移动机器人部署中通过低带宽信道传输，从而支持宏观感知（感知在大尺度空间中的有效覆盖）；生成式特性则通过提供高分辨率重建能力，支撑微观感知（感知在小尺度空间中的精细解析）。这些特性满足了包括多机器人协同探索与灵巧操作在内的多样化机器人应用需求。当前最先进的感知系统通过多条独立管线构建感知模型，重复利用相同的底层传感器数据，导致计算冗余、系统复杂度增加。GIRA通过提供基于高斯混合模型（GMMs）的统一感知建模框架，并贡献了创新性系统设计——利用GPU加速函数将GMM学习速度较现有CPU实现提升10-100倍，弥补了上述不足。鉴于现有基于GMM的开源框架极少，本工作旨在加速技术创新并推动这些技术的广泛采用。