用matlab运行的单目-Lidar融合SLAM实验代码

由于单目-Lidar融合SLAM实验代码可能因为不同的硬件设备和软件环境而有所不同，这里提供一份基于Matlab的单目-Lidar融合SLAM实验代码供参考。

代码主要分为以下几个部分：

1. 相机和Lidar的标定以及数据读取（使用Matlab内置的相机标定工具箱和Velodyne的驱动程序）

2. 特征点提取和匹配（使用SURF算法和暴力匹配）

3. 运动估计和地图构建（使用基于PnP的位姿估计方法和基于Octree的点云地图构建方法）

代码如下：

% Camera-Lidar SLAM with SURF feature matching

clear all
close all

% Camera calibration parameters
load('cam_calib.mat');
K = cameraParams.IntrinsicMatrix';
D = [cameraParams.RadialDistortion cameraParams.TangentialDistortion 0];

% Lidar calibration parameters
lidar = velodyneFileReader('lidar_data.pcap');
lidar.ScanDirection = 'Forward';
lidar.AzimuthOffsetCorrection = -45;
lidar.ElevationAngleOffsetCorrection = 0;

% SURF feature detection
detector = vision.CascadeObjectDetector('NumScaleLevels',4,'ScaleFactor',1.2,'MergeThreshold',30);
pointsPrev = [];
featuresPrev = [];

% Octree parameters
octreeRes = 0.1;
octreeMaxPts = 500;

% SLAM loop
while hasFrame(lidar)
    % Read camera and Lidar data
    img = read(lidar);
    ptCloud = velodyne2pc(img,'Columns',{'x','y','z','intensity'});

    % Camera-Lidar calibration
    [R,t] = extrinsics(ptCloud.Location,K,D);
    T_lidar2cam = [R t';0 0 0 1];

    % SURF feature matching
    bbox = step(detector,img);
    points = detectSURFFeatures(rgb2gray(img),'ROI',bbox);
    [features,points] = extractFeatures(rgb2gray(img),points);
    if ~isempty(pointsPrev)
        indexPairs = matchFeatures(features,featuresPrev,'Method','Exhaustive','MatchThreshold',50);
        matchedPoints = points(indexPairs(:,1),:);
        matchedPointsPrev = pointsPrev(indexPairs(:,2),:);
    end
    pointsPrev = points;
    featuresPrev = features;

    % PnP pose estimation
    if exist('matchedPoints','var')
        worldPoints = lidar2world(matchedPoints.Location,T_lidar2cam)';
        imagePoints = matchedPointsPrev.Location;
        [R_cam2world,t_cam2world] = extrinsics(imagePoints,worldPoints,K);
        T_cam2world = [R_cam2world t_cam2world';0 0 0 1];
    else
        T_cam2world = eye(4);
    end

    % Octree map building
    ptCloudLidar = pointCloud(ptCloud.Location);
    ptCloudLidar = pctransform(ptCloudLidar,@(x) T_lidar2cam*[x';ones(1,size(x,1))]);
    octree = pcfitoctree(ptCloudLidar,'MaxNumPoints',octreeMaxPts,'BlockSize',octreeRes);
    map = octree2world(octree,T_lidar2cam,T_cam2world,octreeRes);

    % Visualization
    figure(1)
    pcshow(ptCloudLidar,'MarkerSize',10)
    hold on
    pcshow(map,'MarkerSize',30,'MarkerColor',[1 0 0])
    hold off
end

代码中的`cam_calib.mat`是用Matlab内置的相机标定工具箱进行的相机标定后保存的文件，`lidar_data.pcap`是Velodyne采集到的Lidar数据。

代码实现了基于SURF特征匹配的单目-Lidar融合SLAM，其中相机和Lidar的标定使用Matlab内置的相机标定工具箱和Velodyne的驱动程序，特征点提取和匹配使用SURF算法和暴力匹配，运动估计和地图构建使用基于PnP的位姿估计方法和基于Octree的点云地图构建方法。在SLAM循环中，程序会读取Lidar数据，进行相机和Lidar的标定，进行SURF特征匹配，进行PnP位姿估计，进行Octree点云地图构建，并可视化结果。

需要注意的是，这份代码只是一个简单的示例，实际情况下需要根据具体的硬件设备和软件环境进行修改和优化。