📸 Explore Python applications for Intel RealSense D435i, featuring depth camera calibration, stream viewing, and real-time 3D visualization tools.
📸 Capture images effortlessly from Intel RealSense cameras using a modular Python app with flexible controls and timestamped saving options.
🛠️ Streamline your computer vision projects with the RealSense Vision Toolkit, a modular solution for Intel RealSense cameras equipped with robust utilities.
XLeRobot: Making AI for Robotics more accessible with end-to-end learning
Non-contact real-time heartbeats and respiration monitoring using Artificial Light Texture (ALT).
This is Handpose ROS Integration Project. This system reconstructs hand joint coordinate frames from 21 hand landmarks using google mediapipe.
ArUco marker detection and pose estimation using camera interfaces. Includes configuration, detection, and pose estimation modules. Supports physical cameras and CoppeliaSim.
This project traces my evolution in object detection. I started with classic color-based segmentation, advanced to integrating a YOLOv8 model, and finally incorporated 3D point cloud processing to create a full detection and tracking pipeline.
This project focused on building a robust, practical utility for sensor data acquisition. I developed a configurable application to manage Intel RealSense cameras, handle multiple data streams, and control hardware features like the IR laser emitter.
The goal here was to build more than just a script; it was to architect a reusable, well-designed toolkit. I applied software engineering principles to create a modular, object-oriented library for RealSense, featuring object detection and coordinate transformations.
This project was developed as part of my hands-on learning journey into computer vision and robotics. It represents my exploration and implementation of Intel RealSense depth camera functionalities, including calibration, stream processing, background removal, and 3D point cloud visualization using Python and OpenCV.
Graduation Project - AMR for supermarket assistance with navigation guidance, product information, and AI self-checkout for elderly/disabled users
This repo provides a practical usage and anaylysis for the fiducial marker systems AprilTag, ArUco, ChAruCo, STag and TopoTag.
A soft gripper system driven by computer vision and point cloud analysis, using shape detection and aligned depth data to estimate object dimensions. Created for the Soft Robotics course project.
Toolkit for fast SGRT application development
Docker environment for Detic ROS driver.
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