yezhicheng99's starred repositories
RoboND-EKFLab
Extended Kalman Filter Localization Lab
robot_pose_ekf
The robot_pose_ekf ROS package applies sensor fusion on the robot IMU and odometry values to estimate its 3D pose.
CMake-tutorial
CMake tutorial: beamer presentation and sample code
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
ORB-SLAM-BIRDVIEW
This repo contains my modification of ORB-SLAM to fuse Birdview.
Birdview_VO
This is my repository for storing Birdview VO
BirdViewTransform
4个不同方向的相机，将其鸟瞰变化后，进行拼接，得到车辆及周围区域的鸟瞰视角图 Put FOUR image(or video stream) from 4 different directions together, and form them into a bird view.
CameraCalibration
Fisheye or Normal Camera Intrinsic and Extrinsic Calibration. Surround Camera Bird Eye View Generator.
surround-view-system-introduction
A full Python implementation for real car surround view system
reflector_ekf_slam
laser reflector and EKF
AutowareArchitectureProposal.proj
This is the source code of the feasibility study for Autoware architecture proposal.
Cpp_Primer_Practice
搞定C++:punch:。C++ Primer 中文版第5版学习仓库，包括笔记和课后练习答案。
free-books
互联网上的免费书籍
UpdatingHDmapByMonoCamera
Updating HD map with RTK-GPS and monocular camera
lidar_rtk_calibration
使用手眼标定法计算Lidar和INS(RTK or IMU)的相对姿态
lidar_align
A simple method for finding the extrinsic calibration between a 3D lidar and a 6-dof pose sensor
scan-matching-localization
Designed two scan matching algorithms, an ICP and NDT, to align point cloud scans from the CARLA simulator and recover the position of a self driving car with LiDAR. My algorithms achieved sufficient accuracy for the entirety of a drive within the simulated environment, updating the vehicle’s location accurately as it moves and obtains new LiDAR data.
SensorFusionND-Lidar-ObstacleDetection
Use Lidar to detect traffic, including cars and trucks, and other obstacles (e.g., poles, traffic signs) on a narrow street.
Road-Lane-Instance-Segmentation-PyTorch
tuSimple dataset road lane instance segmentation with PyTorch, ROS, ENet, SegNet and Discriminative Loss.
LaneNetRos
Ros node to use LaneNet to detect the lane in camera
Ultra-Fast-Lane-Detection
Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)
AutoMerge_Docker
AutoMerge: A Framework for Map Assembling and Smoothing in City-scale Environments
handeye_calib_camodocal
Easy to use and accurate hand eye calibration which has been working reliably for years (2016-present) with kinect, kinectv2, rgbd cameras, optical trackers, and several robots including the ur5 and kuka iiwa.