Chen Bing's repositories
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
Stereo-OF-VO
Containing a wrapper for libviso2, a visual odometry library. The project about Optical flow and ORB and Libviso = visual odometry
uav_integrated_navigation
多旋翼无人机组合导航系统-多源信息融合算法
KalmanFilter-Vehicle-GNSS-INS
In this project, I implemented a Kalman filter on IMU and GPS data recorded from high accuracy sensors.
UCAS-Course
**科学院大学研一课程课件共享项目University of Chinese Academy of Sciences postgraduate course textbook sharing project
RAIM_PANG_NAV
RAIM for PANG NAV a tool for processing GNSS measurements in SPP, including RAIM functionality
goGPS_MATLAB
goGPS MATLAB is an advanced GNSS observation processing software.
Python-CNN-material
Python、神经网络相关资料
Augmented-EKF
AEKF is developed based on the navigation algorithm codes of Johns Hopkins University UAV lab
sensor-fusion
Kalman filters (KF, EKF, UKF), LIDAR object detection
spoofing-detection
Algorithms and simulations on spoofing detection.
keras-yolo3
A Keras implementation of YOLOv3 (Tensorflow backend)
orbslam2-with-LK-optical-flow
This project is modified from orbslam2. All dependencies are consistent with orbslam2
livox_camera_lidar_calibration
Calibrate the extrinsic parameters between Livox LiDAR and camera
MSF_developed
An enhanced multi-sensor fusion framework, based on the ethzasl_msf lib.【基于MSF的增强版多源传感器融合框架 (VSLAM/IMU/GNSS)】
PyTorch-YOLOv3
Minimal PyTorch implementation of YOLOv3
pytorch-YOLOv4
PyTorch ,ONNX and TensorRT implementation of YOLOv4
tensorflow-yolov3
🔥 Pure tensorflow Implement of YOLOv3 with support to train your own dataset