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liyang's Projects

vio icon vio

深蓝学院 手写VIO

vio-1 icon vio-1

Interface PX4 with Realsense T265

vio_data_simulation icon vio_data_simulation

Generate imu data and feature in camera frame. You can use this data to test your VINS.

vio_evaluation icon vio_evaluation

Compare state-of-the-art VO/VSLAM/VIO packages in EuRoC datasets. Algorithms include VINS, MSCKF, ORB-SLAM, SVO2 etc.

viso2 icon viso2

A ROS wrapper for libviso2, a library for visual odometry

visual-gps-slam icon visual-gps-slam

This is a repo for my master thesis research about the Fusion of Visual SLAM and GPS. It contains the research paper, code and other interesting data.

viw-fusion icon viw-fusion

Visual-inertial-wheel fusion odometry, better performance in scenes with drastic changes in light

vld icon vld

Visual Leak Detector for Visual C++ 2008-2015

vo icon vo

A visual odometry demo.

vo-slam-review icon vo-slam-review

SLAM is mainly divided into two parts: the front end and the back end. The front end is the visual odometer (VO), which roughly estimates the motion of the camera based on the information of adjacent images and provides a good initial value for the back end.The implementation methods of VO can be divided into two categories according to whether features are extracted or not: feature point-based methods, and direct methods without feature points. VO based on feature points is stable and insensitive to illumination and dynamic objects

vrep_plugin_imu icon vrep_plugin_imu

Plugin for Vrep that allows to publish imu state as 'sensor_msgs/Imu'

waypoint_nav icon waypoint_nav

This package performs outdoor GPS waypoint navigation using move_base, robot_localization, gmapping and others. It can navigate while building a map, avoiding obstacles, and can navigate continuously between each goal.

xchu_slam icon xchu_slam

a lidar slam system based on multiple loop detection methods and odometry.

yolo-fastestv2 icon yolo-fastestv2

:zap: Based on Yolo's low-power, ultra-lightweight universal target detection algorithm, the parameter is only 250k, and the speed of the smart phone mobile terminal can reach ~300fps+

yolov5-lite icon yolov5-lite

🍅🍅🍅YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 1.7M (int8) and 3.3M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320×320~

zignav icon zignav

ZigNav DEMO, a calibration-free cooperative wireless indoor positioning system

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