AI摘要
本文详细介绍了ArduPilot开发中的视觉伺服技术，包括视觉伺服的分类、坐标系、成像模型、IBVS理论推导以及IBVS面临的挑战和visp实践。文中深入探讨了基于位置(PBVS)和基于图像(IBVS)的视觉伺服，以及它们在无人机视觉伺服中的应用。同时，还涉及了相机坐标系、图像坐标系、像素坐标系的转换关系，以及如何通过视觉特征误差来设计控制律，实现对无人机的精确控制。
控制量是在图像空间中推导得到还是在欧式空间中推导得到，视觉伺服又可以分类为基于位置(PBVS)和基于图像的(IBVS)视觉伺服。
无人船(车)的‘大脑’解剖：Ardupilot控制模式背后的算法逻辑
本文深入解析了Ardupilot作为无人船(车)控制系统的核心算法逻辑，详细介绍了从基础手动控制到高级自主导航的各种控制模式实现。通过分析传感器融合、路径规划和实时控制等关键技术，揭示了开源自动驾驶系统如何将算法转化为精确控制指令，为无人系统开发者提供有价值的参考。
超越遥控器：玩转Ardupilot无人船的DOCK自动停靠与FOLLOW编队模式全攻略（基于OpenMV/Jetson）
本文详细解析了Ardupilot无人船的DOCK自动停靠与FOLLOW编队模式，基于OpenMV/Jetson硬件平台，提供从硬件选型到参数调优的全流程指南。通过AprilTag视觉识别和运动控制协同优化，实现厘米级精度的自主停靠与多船编队，大幅提升无人船的智能化水平与作业效率。
从零搭建无人船：两年实战后，我总结的ArduPilot+Pixhawk避坑全流程
本文详细介绍了从零搭建无人船的全流程，重点分享ArduPilot与Pixhawk硬件组合的实战经验与避坑指南。涵盖硬件选型、软件配置、样船搭建及高级调试等关键环节，特别针对GPS信号丢失、舵机烧毁等常见问题提供解决方案，助力开发者高效完成无人船项目开发。
面向高精度领域的视觉伺服算法汇总与编程
视觉伺服是指将计算机视觉技术应用于控制系统中，通过实时获取图像信息并进行处理分析，以实现对机器人或其他设备的精确位置控制和导航。本文将介绍几种常用的面向高精度领域的视觉伺服算法，并提供相应的源代码。特征点匹配是一种常用的视觉伺服算法，它通过提取图像中的关键特征点，并将其与预先提取的模板特征点进行匹配，以确定物体在图像中的位置和姿态。深度学习算法在视觉伺服中的应用越来越广泛。模板匹配是一种简单而有效的视觉伺服算法，它通过比较输入图像与预先定义的模板图像之间的相似性，来确定物体在图像中的位置和姿态。
ardupilot开发 --- Jetson Orin Nano 后篇
mavlink无人机机载计算机Jetson Orin Nano的开发从0到1，无论是环境安装还是算法应用部署，从视频推拉流、mqtt数据推送、4g图数传到视觉伺服应用，还是OpenCV应用、YOLO算法应用，都在此篇！看不了看亏买不了上当，看它！
ardupilot开发 --- 机载(边缘)计算机-VISP高阶 篇
一个外国的开源视觉伺服算法库VISP，封装了OpenCV、YOLO、PCL等常用的算法库，兼任常见的机器人类型，如mavsdk、ROS等无人小车、无人机、机械臂！
ardupilot开发 --- RealSense-D400 篇
ardupilot开发 --- RealSense-D400
ardupilot相机拍照控制
本节主要记录ardupilot的相机控制逻辑，欢迎批评指正！！！关于ardupilot的相机控制及挂载的控制网上资料很少，本节主要学习下ardupilot的相机快门拍照的控制。添加链接描述相机代码主要用的的库如下所示：SoloGimbal是solo万向节云台的意思通过本节学习可以看出如何进行对相机快门拍照的控制，但是对于多个航点进行自动航拍拍照这个功能需要自己去实现，基本思路就是：当每次到达航点时触发相机快门拍照即可，完成后关闭拍照功能。
ardupilot开发 --- 机载(边缘)计算机-VISP基础 篇
ardupilot开发 --- 机载(边缘)计算机-VISP基础
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RK3568下GPIO模拟串口
在嵌入式开发中，硬件 UART 资源可能被其他外设占用。此时，通过 GPIO 模拟 UART（软串口）成为一种灵活的解决方案。本文基于 RK3568 平台，结合 Linux 内核模块，实现高精度软串口通信，并深入分析关键技术细节。可以看到已经接收到数据了，可以自己创建自定义的设备文件到/dev下，也可以按照串口设备的标准创建一个设备，后面有时间我写一篇和标准串口设备结合的gpio模拟串口文章。RK3568 GPIO 模拟串口（软串口）实现详解。将编译好的驱动放到板子上运行。再写个Makefile。
【OpenClaw从入门到精通】第20篇：OpenClaw实战—从零搭建教育自动化助教系统（作业批改+智能答疑+学情分析）【2026实测】
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Android 编译系统(Build System)剖析
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深度学习工具包 DeepLearningToolbox
深度学习工具包Deprecation notice.-----This toolbox is outdated and no longer maintained.There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [tensorflow](http://www.tensorflow.org/)I would suggest you use one of the tools mentioned above rather than use this toolbox.Best, Rasmus.DeepLearnToolbox================A Matlab toolbox for Deep Learning.Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.A good overview of the theory of Deep Learning theory is[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)```@MASTERSTHESIS\{IMM2012-06284, author = "R. B. Palm", title = "Prediction as a candidate for learning deep hierarchical models of data", year = "2012",}```Contact: rasmusbergpalm at gmail dot comDirectories included in the toolbox-----------------------------------`NN/` - A library for Feedforward Backpropagation Neural Networks`CNN/` - A library for Convolutional Neural Networks`DBN/` - A library for Deep Belief Networks`SAE/` - A library for Stacked Auto-Encoders`CAE/` - A library for Convolutional Auto-Encoders`util/` - Utility functions used by the libraries`data/` - Data used by the examples`tests/` - unit tests to verify toolbox is workingFor references on each library check REFS.mdSetup-----1. Download.2. addpath(genpath('DeepLearnToolbox'));Example: Deep Belief Network---------------------```matlabfunction test_example_DBNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit RBM and visualize its weightsrand('state',0)dbn.sizes = [100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NNrand('state',0)%train dbndbn.sizes = [100 100];opts.numepochs = 1;opts.batchsize = 100;opts.momentum = 0;opts.alpha = 1;dbn = dbnsetup(dbn, train_x, opts);dbn = dbntrain(dbn, train_x, opts);%unfold dbn to nnnn = dbnunfoldtonn(dbn, 10);nn.activation_function = 'sigm';%train nnopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.10, 'Too big error');```Example: Stacked Auto-Encoders---------------------```matlabfunction test_example_SAEload mnist_uint8;train_x = double(train_x)/255;test_x = double(test_x)/255;train_y = double(train_y);test_y = double(test_y);%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN% Setup and train a stacked denoising autoencoder (SDAE)rand('state',0)sae = saesetup([784 100]);sae.ae{1}.activation_function = 'sigm';sae.ae{1}.learningRate = 1;sae.ae{1}.inputZeroMaskedFraction = 0.5;opts.numepochs = 1;opts.batchsize = 100;sae = saetrain(sae, train_x, opts);visualize(sae.ae{1}.W{1}(:,2:end)')% Use the SDAE to initialize a FFNNnn = nnsetup([784 100 10]);nn.activation_function = 'sigm';nn.learningRate = 1;nn.W{1} = sae.ae{1}.W{1};% Train the FFNNopts.numepochs = 1;opts.batchsize = 100;nn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.16, 'Too big error');```Example: Convolutional Neural Nets---------------------```matlabfunction test_example_CNNload mnist_uint8;train_x = double(reshape(train_x',28,28,60000))/255;test_x = double(reshape(test_x',28,28,10000))/255;train_y = double(train_y');test_y = double(test_y');%% ex1 Train a 6c-2s-12c-2s Convolutional neural network %will run 1 epoch in about 200 second and get around 11% error. %With 100 epochs you'll get around 1.2% errorrand('state',0)cnn.layers = { struct('type', 'i') %input layer struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %sub sampling layer struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer struct('type', 's', 'scale', 2) %subsampling layer};cnn = cnnsetup(cnn, train_x, train_y);opts.alpha = 1;opts.batchsize = 50;opts.numepochs = 1;cnn = cnntrain(cnn, train_x, train_y, opts);[er, bad] = cnntest(cnn, test_x, test_y);%plot mean squared errorfigure; plot(cnn.rL);assert(er<0.12, 'Too big error');```Example: Neural Networks---------------------```matlabfunction test_example_NNload mnist_uint8;train_x = double(train_x) / 255;test_x = double(test_x) / 255;train_y = double(train_y);test_y = double(test_y);% normalize[train_x, mu, sigma] = zscore(train_x);test_x = normalize(test_x, mu, sigma);%% ex1 vanilla neural netrand('state',0)nn = nnsetup([784 100 10]);opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samples[nn, L] = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.08, 'Too big error');%% ex2 neural net with L2 weight decayrand('state',0)nn = nnsetup([784 100 10]);nn.weightPenaltyL2 = 1e-4; % L2 weight decayopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex3 neural net with dropoutrand('state',0)nn = nnsetup([784 100 10]);nn.dropoutFraction = 0.5; % Dropout fraction opts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex4 neural net with sigmoid activation functionrand('state',0)nn = nnsetup([784 100 10]);nn.activation_function = 'sigm'; % Sigmoid activation functionnn.learningRate = 1; % Sigm require a lower learning rateopts.numepochs = 1; % Number of full sweeps through dataopts.batchsize = 100; % Take a mean gradient step over this many samplesnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex5 plotting functionalityrand('state',0)nn = nnsetup([784 20 10]);opts.numepochs = 5; % Number of full sweeps through datann.output = 'softmax'; % use softmax outputopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, train_x, train_y, opts);[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');%% ex6 neural net with sigmoid activation and plotting of validation and training error% split training data into training and validation datavx = train_x(1:10000,:);tx = train_x(10001:end,:);vy = train_y(1:10000,:);ty = train_y(10001:end,:);rand('state',0)nn = nnsetup([784 20 10]); nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[](https://bitdeli.com/free "Bitdeli Badge")
文档：亲测可用解决vnc灰屏，黑屏，鼠标大黑叉情况
主要解决vnc-viewer灰屏，黑屏，鼠标大黑叉等现象，详细见文档。
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(Java SE Runtime Environment 9.0) 9.0u175 官方最新版 【x86|x64】
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