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jinzhu6's Projects

ros_astra_launch icon ros_astra_launch

Launch files to open an Astra device and load all nodelets to convert raw depth/RGB/IR streams to depth images, disparity images, and (registered) point clouds.

rr_auto_dock icon rr_auto_dock

This package allows you to autonomously dock any of the open rovers using just a camera.

rsslam_ws icon rsslam_ws

ROS Workspace for SLAM using RTab-MAP ros-pkg on Realsense d435i

sa icon sa

EKF-SLAM with arUco visual markers for an ITER remote transport cask prototype.

segmentation icon segmentation

A ROS package for cylinder, box, and sphere segmentation and pose estimation in an interactive or non-interactive way

sensor_box_itv icon sensor_box_itv

ROS workspace and tutorial for the "Sensor Box" containing Velodyne, RGB and ToF cameras

shape_based_matching icon shape_based_matching

try to implement halcon shape based matching, refer to machine vision algorithms and applications, page 317 3.11.5, written by halcon engineers

sick_visionary_t_test icon sick_visionary_t_test

directly get polar2d data from sick visionary t, max 30 points. remember to change the data reduction mode

slam-pose_estimation icon slam-pose_estimation

Filter based non-linear robot pose and velocity estimation using an arbitrary number of state measurements

solarframework icon solarframework

SolAR is an open-source framework released under Apache license 2.0 making it possible to easily create your own camera pose estimation solution to develop augmented reality applications.

sp_segmenter icon sp_segmenter

Superpixel-based semantic segmentation, with object pose estimation and tracking. Provided as a ROS package.

spheres_localization icon spheres_localization

Performs pose estimation by matching SIFT features from a phone's camera to a point cloud generated by Kinect Fusion.

stackdemcubes icon stackdemcubes

An application created for a unit at University, control a forklift and "stackdemcubes"

studies-on-driving-behaviors icon studies-on-driving-behaviors

Data analysis in “Statistical methods in Research” course in Spring 2017 using R and R studio. Performing quality control on the dataset for Heart rate, breathing rate, perinasal perspiration, Palm EDA sensors and acceleration signals. Based on the Heart rate, breathing rate, perinasal perspiration, and performance measures of 88 subjects, statistical interpretation, and deduction using paired t-test whether subjects felt stressed on applying Emotional stress, Cognitive stress, and external disturbances while driving. We need to determine which of these measures signify significant elevation of stress and which are not, and comment on the results.

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