You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: main.m
    path of file: ./repos/hongbomiao.com/automobile/build-map-by-lidar-point-cloud
    
    the code of the file until where you have to start completion: % https://www.mathworks.com/help/driving/ug/build-a-map-from-lidar-data-using-slam.html

% Process 3-D lidar data from a sensor mounted on a vehicle to progressively build a map and estimate the trajectory of a vehicle using simultaneous localization and mapping (SLAM).
% In addition to 3-D lidar data, an inertial navigation sensor (INS) is also used to help build the map.
% Maps built this way can facilitate path planning for vehicle navigation or can be used for localization.

% Use 3-D lidar data and IMU readings to progressively build a map of the environment traversed by a vehicle.
% While this approach builds a locally consistent map, it is suitable only for mapping small areas.
% Over longer sequences, the drift accumulates into a significant error.
% To overcome this limitation, we recognize previously visited places and tries to correct for the accumulated drift using the graph SLAM approach.

% https://www.mrt.kit.edu/z/publ/download/velodyneslam/dataset.html
baseDownloadURL = 'https://www.mrt.kit.edu/z/publ/download/velodyneslam/data/scenario1.zip';
dataFolder = fullfile(tempdir, 'kit_velodyneslam_data_scenario1', filesep);
options = weboptions('Timeout', Inf);

zipFileName = dataFolder + "scenario1.zip";

% Get the full file path to the PNG files in the scenario1 folder.
pointCloudFilePattern = fullfile(dataFolder, 'scenario1', 'scan*.png');

folderExists = exist(dataFolder, 'dir');
fileCount = 2513;
if ~folderExists
    mkdir(dataFolder);
    disp('Downloading ...');
    websave(zipFileName, baseDownloadURL, options);
    unzip(
    
function creates a node for each view, and an edge for each connection in the view set.
% To optimize the pose graph, we can use the `optimizePoseGraph` function.
%
% A key aspect contributing to the effectiveness of graph SLAM in correcting drift is the accurate detection of loops, that is, places that have been previously visited.
% This is called "loop closure detection" or "place recognition".
% Adding edges to the pose graph corresponding to loop closures provides a contradictory measurement for the connected node poses, which can be resolved during pose graph optimization.
%
% Loop closures can be detected using descriptors that characterize the local environment visible to the Lidar sensor.
% The "Scan Context" descriptor is one such descriptor that can be computed from a point cloud using the `scanContextDescriptor` function.
% This uses a `scanContextLoopDetector` object to manage the scan context descriptors that correspond to each view.
% It uses the `detectLoop` object function to detect loop closures with a two phase descriptor search algorithm.
% In the first phase, it computes the ring key subdescriptors to find potential loop candidates.
% In the second phase, it classifies views as loop closures by thresholding the scan context distance.

% Set random seed to ensure reproducibility
rng(0);

% Create an empty view set
vSet = pcviewset;

% Create a loop closure detector
loopDetector = scanContextLoopDetector;

% Create a figure for view set display
hFigBefore = figure('Name', 'View Set Display');
hAxBefore = axes(hFigBefore);

% Initialize transformations
absTform = rigidtform3d; % Absolute transformation to reference frame
relTform = rigidtform3d; % Relative transformation between successive scans

maxTolerableRMSE  = 3; % Maximum allowed RMSE for a loop closure candidate to be accepted

viewId = 1;
for n = 1:skipFrames:numFrames
    % Read point cloud
    fileName = pointCloudTable.PointCloudFileName{n};
    ptCloudOrig = readPointCloudFromFile(fileName);

    % Process point cloud
    % - Segment and remove ground plane
    % - Segment and remove ego vehicle
    ptCloud = processPointCloud(ptCloudOrig);

    % Downsample the processed point cloud
    ptCloud = pcdownsample(ptCloud, "random", downsamplePercent);

    firstFrame = (n == 1);
    if firstFrame
        % Add first point cloud scan as a view to the view set
        vSet = addView(vSet, viewId, absTform, "PointCloud", ptCloudOrig);

        % Extract the scan context descriptor from the first point cloud
        descriptor = scanContextDescriptor(ptCloudOrig);

        % Add the first descriptor to the loop closure detector
        addDescriptor(loopDetector, viewId, descriptor);

        viewId = viewId + 1;
        ptCloudPrev = ptCloud;
        continue
    end