awesome-repositories.com
Blog
MCP
awesome-repositories.com

Entdecke die besten Open-Source-Repositories mit KI-gestützter Suche.

EntdeckenKuratierte SuchenOpen-Source-AlternativenSelf-hosted SoftwareBlogSitemap
ProjektMCP-ServerÜber unsRanking-MethodikPresse
RechtlichesDatenschutzAGB
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
·

3 Repos

Awesome GitHub RepositoriesPerception Pipelines

Modular sequences of data processing stages specifically for environmental sensing and world modeling.

Distinct from Pipeline Component Modularization: Distinct from Pipeline Component Modularization: specifically targets the perception-to-world-model pipeline in robotics rather than general data processing.

Explore 3 awesome GitHub repositories matching software engineering & architecture · Perception Pipelines. Refine with filters or upvote what's useful.

Awesome Perception Pipelines GitHub Repositories

Finde die besten Repos mit KI.Wir suchen mit KI nach den am besten passenden Repositories.
  • cpfl/autowareAvatar von CPFL

    CPFL/Autoware

    11,716Auf GitHub ansehen↗

    Autoware is a modular autonomous driving stack and open-source platform for advanced driver assistance systems. It functions as an integrated operating environment that manages the full pipeline from sensor data processing to vehicle actuation, utilizing the ROS 2 robotics framework for distributed communication and hardware abstraction. The system provides a comprehensive software architecture to enable autonomous driving across various vehicle platforms. It coordinates perception, planning, and control systems to operate vehicles without human intervention. The platform covers several core

    Processes raw sensor data through discrete stages of detection and tracking to build a consistent world model.

    Dockerfile
    Auf GitHub ansehen↗11,716
  • udacity/self-driving-carAvatar von udacity

    udacity/self-driving-car

    6,312Auf GitHub ansehen↗

    This is an open-source autonomous driving perception pipeline that processes camera and lidar sensor data to detect, track, and fuse objects in real-world driving environments. The project integrates an end-to-end perception workflow combining sensor calibration, deep learning object detection, Kalman filter tracking, and sensor fusion for robust scene understanding. The pipeline includes camera calibration tools to remove lens distortion from raw images, deep learning model training for object classification and detection, and multi-object tracking using Kalman filters with data association

    Provides an open-source pipeline for processing camera and lidar data to detect, track, and fuse objects.

    Jupyter Notebook
    Auf GitHub ansehen↗6,312
  • realsenseai/realsense-rosAvatar von realsenseai

    realsenseai/realsense-ros

    3,365Auf GitHub ansehen↗

    This project is a ROS2 depth camera driver that streams synchronized RGB, depth, infrared, and IMU data from Intel RealSense sensors as ROS2 topics. It functions as a managed camera interface using lifecycle nodes with explicit state transitions to ensure deterministic startup and shutdown within robotic systems. The driver includes an RGBD perception pipeline that aligns depth to color and generates 3D point clouds for spatial analysis. It features a depth camera calibration tool that provides ROS2 services for reading and writing on-device calibration and safety configuration parameters. T

    Implements a modular sequence of data processing stages to transform depth data into point clouds for spatial analysis.

    Python
    Auf GitHub ansehen↗3,365
  1. Home
  2. Software Engineering & Architecture
  3. Modular Design Patterns
  4. Pipeline Component Modularization
  5. Perception Pipelines

Unter-Tags erkunden

  • VideoModular pipelines for connecting detection, tracking, and segmentation components in video analysis. **Distinct from Perception Pipelines:** Distinct from general perception pipelines: specifically targets video-based perception workflows.