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GIRA: Gaussian Mixture Models for Inference and Robot Autonomy

Kshitij Goel, Wennie Tabib

TL;DR

The paper addresses the need for compact, high-fidelity perceptual representations that scale to multi-robot deployments while preserving fine detail. It introduces GIRA, an open-source framework that uses $4D$ Self-Organizing Gaussian Mixture Models to unify point-cloud reconstruction, pose estimation, and occupancy modeling, with GPU-accelerated learning achieving up to 10-100x speedups. Key contributions include CPU and GPU implementations of SOGMMs, distribution-to-distribution registration, and occupancy sampling with ray tracing, all integrated in ROS/ROS2 workflows. The work aims to accelerate innovation and adoption of compact perceptual representations for real-time robotics in exploration, manipulation, and general autonomy.

Abstract

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.

GIRA: Gaussian Mixture Models for Inference and Robot Autonomy

TL;DR

The paper addresses the need for compact, high-fidelity perceptual representations that scale to multi-robot deployments while preserving fine detail. It introduces GIRA, an open-source framework that uses Self-Organizing Gaussian Mixture Models to unify point-cloud reconstruction, pose estimation, and occupancy modeling, with GPU-accelerated learning achieving up to 10-100x speedups. Key contributions include CPU and GPU implementations of SOGMMs, distribution-to-distribution registration, and occupancy sampling with ray tracing, all integrated in ROS/ROS2 workflows. The work aims to accelerate innovation and adoption of compact perceptual representations for real-time robotics in exploration, manipulation, and general autonomy.

Abstract

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.
Paper Structure (11 sections, 3 equations, 7 figures)

This paper contains 11 sections, 3 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Design
  4. GIRA Framework
  5. GIRA Reconstruction
  6. GIRA Registration
  7. GIRA Occupancy Modeling
  8. Implementation Details
  9. Gaussian Blurring Mean Shift
  10. Expectation Maximization
  11. Conclusion

Figures (7)

  • Figure 1: GIRA has been deployed on size, weight, and power constrained aerial systems in real-world and unstructured environments. (Top left) A single aerial robot flies through an industrial tunnel and (top center) generates a high-fidelity Gaussian mixture model (GMM) map of the environment. (Top right) A close-up view of the reconstructed area around the robot. (Bottom left and bottom center) A team of two robots fly through a dark tunnel environment and produce a (bottom right) map, which is resampled from the underlying GMM and colored red or blue according to which robot took the observation. Videos of these experiments are available at: https://youtu.be/qkbxfxgCoV0 and https://youtu.be/t9iYd33oz3g.
  • Figure 2: An example workflow for GIRA Reconstruction \ref{['ssec:gira3d-reconstruction']}. The input is a depth-intensity point cloud shown in \ref{['sfig:input']}. The resulting model can be resampled to generate novel 4D points \ref{['sfig:resampled']} or be used to infer expected intensity values at known 3D locations \ref{['sfig:inferred']}.
  • Figure 3: Comparison of SOGMM computation time via GIRA Reconstruction on the target platforms listed in \ref{['sfig:target-platforms']}. In \ref{['sfig:rtx3090-sogmm']} and \ref{['sfig:rtx3060-sogmm']} the GPU-accelerated case on the desktop platforms provides more than an order of magnitude improvement in timing compared to the CPU-only case for most image sizes. The results of the embedded platforms shown in \ref{['sfig:orin-sogmm']}, \ref{['sfig:xavier-sogmm']} and \ref{['sfig:tx2-sogmm']} demonstrate that the relative performance improvements seem to degrade with increasing SWaP constraints. In any case, \ref{['sfig:ryzen-scikit']} shows that our CPU implementation performs nearly an order of magnitude faster than a reference SOGMM implementation using scikit-learn.
  • Figure 4: The point clouds in \ref{['sfig:misaligned']} are originally misaligned. \ref{['sfig:aligned']} The code in \ref{['ssec:gira3d-registration']} estimates the SE(3) transform to align them.
  • Figure 5: The trajectories reconstructed using \ref{['sfig:no-loop-closure']} frame-to-frame registration and \ref{['sfig:loop-closure']} with loop closure is enabled are shown with the pointclouds plotted.
  • ...and 2 more figures