[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

Build librealsense 2.0 library on the NVIDIA Jetson AGX Xavier Developer Kit. Intel RealSense D400 series cameras.

Build librealsense 2.0 library on the NVIDIA Jetson TX Development kit. Intel RealSense D400 series cameras.

RaspberryPi3(Raspbian Stretch) + MobileNetv2-SSDLite(Tensorflow/MobileNetv2SSDLite) + RealSense D435 + Tensorflow1.11.0 + without Neural Compute Stick(NCS)

土炮智能機器手視覺系統，結合Intel OpenVINO NCS, RealSense D435完成採收小蕃茄直交式機器手臂視覺系統概念機。

[DEMO] links Intel RealSense T265 (tracking-camera) & Intel RealSense D435i (depth-camera) via roboter-operating-system (ROS) for generating a trackable point-cloud image dataset in the "real world"

ROS2 Package for instance segmentation using the YoloV8 segmentation model and a Intel Realsense D435 camera

Using a Intel Realsense T265 to build an occupancy grid and autonomously navigate around using move_base

Intel RealSense Depth Camera D435i D455 D435 D415

此代码库允许人们在 ROS2 Humble 版本中使用 apriltag 代码进行正向定位和姿态的自动调整，并且已在实车上成功测试，但可能有一些繁琐的调整过程，参数需要调整以进一步优化。

How to set up ROS Noetic, Gazebo, MavROS, MavLink, PX4 Autopilot, and an Iris drone with a D435 camera.

Real-time streaming (AWS kvs) & object detection & semantic segmentation using RealSense D435i on Jetson Nano

A modular software architecture for Automatic Plant Phenotyping

本项目基于 LiDAR、RGB-D 相机与 IMU 的多模态融合技术，为实现了移动小车的 三维建图与自主导航为目标。系统采用 Unitree L1 激光雷达 提供高精度几何结构信息，结合 Intel RealSense D435 相机 提供彩色与深度数据，并融合 MPU6050 惯性测量单元 (IMU) 实现姿态与运动状态的补偿与优化。 通过 FAST-LIO2 与 RTAB-Map 的多源融合建图算法，系统能够在复杂环境下实现高精度的环境感知、鲁棒的位姿估计与实时自主导航功能，为 多传感器融合 SLAM 与智能移动机器人研究 提供了一个可靠的实验与开发平台。

2nd Assignment of Computer Vision by José Rosa and Ricardo Silva

Using Intel Realsense T265 and D435 to navigate UAV indoor

使用Realsense D435i实现机器人目标跟随

Object detection and tracker utilising RealSense D435 Depth Camera on detecting features and object relation. Python is the main language utilised for this project and OpenCV as the main Computer Vision Framework.