问具有可变采样率的周期数据平均

EN

% Assume that the first point is the start point (t = 0)
start_point = path(1,:);

% Compute the distance to this point for all data points
distances = sqrt(sum(bsxfun(@minus, path, start_point).^2, 2));

% Find the minima of this curve (these are all the times that the car passed the start)
% We apply some smoothing to get rid of necessary noise. Really depends on your data
[~, locs] = findpeaks(smooth(-distances, 20));

% Make sure we include the first and last point
locs = [1; locs; numel(distances)];

% Desired samples for each loop
nSamples = 1000;

% Pre-allocate the outputs
xpoints = zeros(numel(locs) - 1, nSamples);
ypoints = zeros(numel(locs) - 1, nSamples);

for k = 1:(numel(locs) - 1)
    % Get the coordinates recorded for this particular loop
    loop_points = path(locs(k):locs(k+1),:);

    % Compute the cumulative arc-length using these points
    arc_length = cumsum([0; sum(diff(loop_points, [], 1).^2, 2)]);

    % Normalize the arc_length between 0 and 1
    arc_length = arc_length ./ arc_length(end);

    % Interpolate along the curve
    xpoints(k,:) = interp1(arc_length, loop_points(:,1), linspace(0, 1, nSamples));
    ypoints(k,:) = interp1(arc_length, loop_points(:,2), linspace(0, 1, nSamples));
end

% Average all the x and y locations
X = mean(xpoints, 1);
Y = mean(ypoints, 1);

plot(X, Y)

nLoops = 10;

x = [];
y = [];

for k = 1:nLoops
    nSamples = randi([50, 70]);

    t = linspace(0, 2*pi, nSamples + 1);
    t(end) = [];

    x = cat(1, x(:), cos(t(:)) + 0.1 * (rand(size(t(:))) - 0.5));
    y = cat(1, y(:), sin(t(:)) + 0.1 * (rand(size(t(:))) - 0.5));
end

path = [x(:), y(:)];