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kailuodewangzi's Projects

ahrs icon ahrs

Attitude and Heading Reference System using MATLAB as simple as possible

common_msgs icon common_msgs

Commonly used messages in ROS. Includes messages for actions (actionlib_msgs), diagnostics (diagnostic_msgs), geometric primitives (geometry_msgs), robot navigation (nav_msgs), and common sensors (sensor_msgs), such as laser range finders, cameras, point clouds.

ekf_fusion icon ekf_fusion

Using an Extended Kalman Filter to calculate a UAV's pose from IMU and GPS data.

eskf icon eskf

ROS Error-State Kalman Filter based on PX4/ecl. Performs GPS/Magnetometer/Vision Pose/Optical Flow/RangeFinder fusion with IMU

evo icon evo

Python package for the evaluation of odometry and SLAM

extended_kalman_filter icon extended_kalman_filter

基于扩展卡尔曼滤波(EKF)的四旋翼无人机姿态估计 Extended Karman Filtering (EKF) Implemention in Uavs Using Matlab

fusion-ekf icon fusion-ekf

An extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements.

gnss-ins-sim icon gnss-ins-sim

Open-source GNSS + inertial navigation, sensor fusion simulator. Motion trajectory generator, sensor models, and navigation

kalibr_allan icon kalibr_allan

IMU Allan standard deviation charts for use with Kalibr and inertial kalman filters.

kalmanfilter icon kalmanfilter

This is a Kalman filter used to calculate the angle, rate and bias from from the input of an accelerometer/magnetometer and a gyroscope.

multi_sensor_fusion icon multi_sensor_fusion

Multi-Sensor Fusion (GNSS, IMU, Camera) 多源多传感器融合定位 GPS/INS组合导航 PPP/INS紧组合

orb_slam2_ssd_semantic icon orb_slam2_ssd_semantic

动态语义SLAM 目标检测+VSLAM+光流/多视角几何动态物体检测+octomap地图+目标数据库

pangolin icon pangolin

Pangolin is a lightweight portable rapid development library for managing OpenGL display / interaction and abstracting video input.

pl-vio icon pl-vio

monocular visual inertial system with point and line features

robust-attitude-controller-for-uav-using-dynamic-inversion-and-extended-state-observer-controller icon robust-attitude-controller-for-uav-using-dynamic-inversion-and-extended-state-observer-controller

A robust feedback linearization controller is presented for attitude control of an unmanned aerial vehicle (UAV). The objective of this controller is to make the roll angle, pitch angle, and yaw angle track the given trajectories(commands) respectively. This design is developed using dynamic inversion and extended state observer (ESO). Firstly, dynamic inversion is used to linearize and decouple UAV attitude system into three single-input-single-output (SISO) systems, then three proportional-derivative (PD) controllers are designed for these linearized systems. Extended state observers are used to estimate and compensate unmodeled dynamics and extent disturbances. Simulation results show that the proposed controller is effective and robust.

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