MichaelBeechan/rtklibdemo5

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Created about 2 years ago
Updated over 1 year ago

MichaelBeechan/rtklibdemo5

MichaelBeechan

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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

ThunderNet-Review

Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational cost, which hinders them from real-time inference in computation-constrained scenarios. In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight twostage detector named ThunderNet. In the backbone part, we analyze the drawbacks in previous lightweight backbones and present a lightweight backbone designed for object detection. In the detection part, we exploit an extremely efficient RPN and detection head design. To generate more discriminative feature representation, we design two efficient architecture blocks, Context Enhancement Module and Spatial Attention Module. At last, we investigate the balance between the input resolution, the backbone, and the detection head. Compared with lightweight one-stage detectors, ThunderNet achieves superior performance with only 40% of the computational cost on PASCAL VOC and COCO benchmarks. Without bells and whistles, our model runs at 24.1 fps on an ARM-based device. To the best of our knowledge, this is the first real-time detector reported on ARM platforms. Code will be released for paper reproduction.

Stereo-OF-VO

Containing a wrapper for libviso2, a visual odometry library. The project about Optical flow and ORB and Libviso = visual odometry

PPP-RTK

SPP、RTD、PPP、RTK、PPP-RTK、RAIM、ARAIM et al

ImageProcessing_Mathematical_Modeling

“华为杯”第十四届中国研究生数学建模竞赛（图像处理篇）——获全国二等奖(基于监控视频的前景目标提取)

RTCM3.3

Learning_TensorFlow-Kaggle_MNIST

一步步带你通过项目（MNIST手写识别）学习入门TensorFlow以及神经网络的知识

RAIM

RAIM（Receiver Autonomous Integrity Monitoring）

TensorMask-Review

Sliding-window object detectors that generate boundingbox object predictions over a dense, regular grid have advanced rapidly and proven popular. In contrast, modern instance segmentation approaches are dominated by methods that first detect object bounding boxes, and then crop and segment these regions, as popularized by Mask R-CNN. In this work, we investigate the paradigm of dense slidingwindow instance segmentation, which is surprisingly underexplored. Our core observation is that this task is fundamentally different than other dense prediction tasks such as semantic segmentation or bounding-box object detection, as the output at every spatial location is itself a geometric structure with its own spatial dimensions. To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors. We demonstrate that the tensor view leads to large gains over baselines that ignore this structure, and leads to results comparable to Mask R-CNN. These promising results suggest that TensorMask can serve as a foundation for novel advances in dense mask prediction and a more complete understanding of the task. Code will be made available.

RAIM_PANG_NAV

RAIM for PANG NAV a tool for processing GNSS measurements in SPP, including RAIM functionality

MPU6050_Kalman_PWM_remote

MPU6050 Kalman PWM_remote

SLAM-for-Paper

SLAM related papers and mathematical materials

SLAM-for-Matlab

SLAM related matlab

Matlab-From-Zero-To-One

MATLAB quickstart

Optical_Flow_Based_on_Duality_Method

The three elements of optical flow are as follows: optical flow produces velocity field; it is a carrier with information and optical properties such as pixels; it is a projection imaging of 3D scene motion to two-dimensional plane. It is the instantaneous velocity field generated by the motion of pixels with grayscale on the plane of the image.

C_plus_Plus_From_Zero_To_One

C_plus_Plus_From_Zero_To_One, Here are some exercises about C++, From Netease cloud class to Lao Jiu Jun

Python-CNN-material

Python、神经网络相关资料

Jupyter Notebook

VisualOdometry_BasedOnSURF

%% Estimating the pose of the second view relative to the first view %% Bootstrapping estimating camera trajectory using global bundle adjustment %% Estimating remaining camera trajectory using windowed bundle adjustment

Data_Structures_and_Algorithms

Data Structures and Algorithms with C program

ORBSLAM2

pandas_Python_Data_crawler

This is my first pandas-based web crawler project

Jupyter Notebook