Merge pull request 'varnashova88-patch-1' (#1) from varnashova88-patch-1 into develop

Reviewed-on: #1

processing/calculations.cpp

@@ -0,0 +1,360 @@
+#include <iostream>
+#include <fstream>
+#include <sstream>
+#include <vector>
+#include <cmath>
+#include <algorithm>
+#include <Eigen/Dense>
+
+using namespace std;
+using namespace Eigen;
+
+// СТРУКТУРЫ
+
+struct IMUData {
+    double t;
+    Vector3d acc;   // g
+    Vector3d gyro;  // deg/s
+};
+
+struct LidarData {
+    double t;
+    double angle;
+    double distance; // mm
+};
+
+struct State {
+    VectorXd x;
+    MatrixXd P;
+};
+
+// ЧТЕНИЕ CSV
+
+vector<IMUData> readIMU(const string& filename) {
+    vector<IMUData> data;
+    ifstream file(filename);
+    string line;
+
+    getline(file, line);
+
+    while (getline(file, line)) {
+        stringstream ss(line);
+        string val;
+
+        IMUData d;
+
+        getline(ss, val, ','); d.t = stod(val);
+        getline(ss, val, ','); d.acc.x() = stod(val);
+        getline(ss, val, ','); d.acc.y() = stod(val);
+        getline(ss, val, ','); d.acc.z() = stod(val);
+        getline(ss, val, ','); d.gyro.x() = stod(val);
+        getline(ss, val, ','); d.gyro.y() = stod(val);
+        getline(ss, val, ','); d.gyro.z() = stod(val);
+
+        data.push_back(d);
+    }
+
+    return data;
+}
+
+vector<LidarData> readLidar(const string& filename) {
+    vector<LidarData> data;
+    ifstream file(filename);
+    string line;
+
+    getline(file, line);
+
+    while (getline(file, line)) {
+        stringstream ss(line);
+        string val;
+
+        LidarData d;
+
+        getline(ss, val, ','); d.t = stod(val);
+        getline(ss, val, ','); d.angle = stod(val);
+        getline(ss, val, ','); d.distance = stod(val);
+
+        data.push_back(d);
+    }
+
+    return data;
+}
+
+// ПОВОРОТ
+
+Matrix3d rotationMatrix(double roll, double pitch, double yaw) {
+    AngleAxisd rx(roll, Vector3d::UnitX());
+    AngleAxisd ry(pitch, Vector3d::UnitY());
+    AngleAxisd rz(yaw, Vector3d::UnitZ());
+    return (rz * ry * rx).toRotationMatrix();
+}
+
+// IMU PREDICT
+
+void predict(State& s, const IMUData& imu, double dt) {
+    VectorXd& x = s.x;
+
+    Vector3d pos = x.segment<3>(0);
+    Vector3d vel = x.segment<3>(3);
+    Vector3d angles = x.segment<3>(6);
+
+    Vector3d acc = imu.acc * 9.81;
+    Vector3d gyro = imu.gyro * M_PI / 180.0;
+
+    Matrix3d R = rotationMatrix(angles[0], angles[1], angles[2]);
+
+    Vector3d g(0, 0, 9.81);
+    Vector3d acc_world = R * acc - g;
+
+    vel += acc_world * dt;
+    pos += vel * dt + 0.5 * acc_world * dt * dt;
+    angles += gyro * dt;
+
+    x.segment<3>(0) = pos;
+    x.segment<3>(3) = vel;
+    x.segment<3>(6) = angles;
+
+    MatrixXd F = MatrixXd::Identity(15, 15);
+    F.block<3, 3>(0, 3) = Matrix3d::Identity() * dt;
+
+    MatrixXd Q = MatrixXd::Identity(15, 15) * 0.01;
+
+    s.P = F * s.P * F.transpose() + Q;
+}
+
+// ЛИДАР → МИР
+
+Vector3d lidarToWorld(const State& s, const LidarData& l) {
+    double angle = l.angle * M_PI / 180.0;
+    double dist = l.distance / 1000.0;
+
+    Vector3d local;
+    local << dist * cos(angle), dist* sin(angle), 0;
+
+    Vector3d pos = s.x.segment<3>(0);
+    Vector3d ang = s.x.segment<3>(6);
+
+    Matrix3d R = rotationMatrix(ang[0], ang[1], ang[2]);
+
+    return pos + R * local;
+}
+
+// KD-TREE
+
+struct KDNode {
+    Vector3d point;
+    KDNode* left;
+    KDNode* right;
+    int axis;
+
+    KDNode(Vector3d p, int ax) : point(p), left(nullptr), right(nullptr), axis(ax) {}
+};
+
+KDNode* buildKDTree(vector<Vector3d>& pts, int depth = 0) {
+    if (pts.empty()) return nullptr;
+
+    int axis = depth % 3;
+
+    sort(pts.begin(), pts.end(),
+        [axis](const Vector3d& a, const Vector3d& b) {
+            return a[axis] < b[axis];
+        });
+
+    int mid = pts.size() / 2;
+
+    KDNode* node = new KDNode(pts[mid], axis);
+
+    vector<Vector3d> left(pts.begin(), pts.begin() + mid);
+    vector<Vector3d> right(pts.begin() + mid + 1, pts.end());
+
+    node->left = buildKDTree(left, depth + 1);
+    node->right = buildKDTree(right, depth + 1);
+
+    return node;
+}
+
+void nearestSearch(KDNode* node, const Vector3d& target,
+    Vector3d& best, double& best_dist) {
+    if (!node) return;
+
+    double d = (node->point - target).squaredNorm();
+    if (d < best_dist) {
+        best_dist = d;
+        best = node->point;
+    }
+
+    int axis = node->axis;
+
+    KDNode* near = target[axis] < node->point[axis] ? node->left : node->right;
+    KDNode* far = target[axis] < node->point[axis] ? node->right : node->left;
+
+    nearestSearch(near, target, best, best_dist);
+
+    double diff = target[axis] - node->point[axis];
+    if (diff * diff < best_dist) {
+        nearestSearch(far, target, best, best_dist);
+    }
+}
+
+// ICP
+
+Matrix3d computeRotation(const vector<Vector3d>& src,
+    const vector<Vector3d>& dst) {
+
+    Vector3d c1 = Vector3d::Zero();
+    Vector3d c2 = Vector3d::Zero();
+
+    for (size_t i = 0;i < src.size();i++) {
+        c1 += src[i];
+        c2 += dst[i];
+    }
+
+    c1 /= src.size();
+    c2 /= dst.size();
+
+    Matrix3d H = Matrix3d::Zero();
+
+    for (size_t i = 0;i < src.size();i++) {
+        H += (src[i] - c1) * (dst[i] - c2).transpose();
+    }
+
+    JacobiSVD<Matrix3d> svd(H, ComputeFullU | ComputeFullV);
+    return svd.matrixV() * svd.matrixU().transpose();
+}
+
+Vector3d computeTranslation(const vector<Vector3d>& src,
+    const vector<Vector3d>& dst,
+    const Matrix3d& R) {
+
+    Vector3d c1 = Vector3d::Zero();
+    Vector3d c2 = Vector3d::Zero();
+
+    for (size_t i = 0;i < src.size();i++) {
+        c1 += src[i];
+        c2 += dst[i];
+    }
+
+    c1 /= src.size();
+    c2 /= dst.size();
+
+    return c2 - R * c1;
+}
+
+vector<Vector3d> downsample(const vector<Vector3d>& pts, int step = 5) {
+    vector<Vector3d> out;
+    for (size_t i = 0;i < pts.size();i += step)
+        out.push_back(pts[i]);
+    return out;
+}
+
+void ICP_fast(vector<Vector3d>& src,
+    vector<Vector3d>& dst,
+    Matrix3d& R, Vector3d& t) {
+
+    R = Matrix3d::Identity();
+    t = Vector3d::Zero();
+
+    vector<Vector3d> dst_copy = dst;
+    KDNode* tree = buildKDTree(dst_copy);
+
+    for (int iter = 0; iter < 10; iter++) {
+
+        vector<Vector3d> matched;
+
+        for (auto& p : src) {
+            Vector3d best;
+            double best_dist = 1e9;
+
+            nearestSearch(tree, p, best, best_dist);
+
+            if (best_dist < 0.5)
+                matched.push_back(best);
+        }
+
+        if (matched.size() < 10) break;
+
+        Matrix3d dR = computeRotation(src, matched);
+        Vector3d dt = computeTranslation(src, matched, dR);
+
+        for (auto& p : src)
+            p = dR * p + dt;
+
+        R = dR * R;
+        t = dR * t + dt;
+    }
+}
+
+// MAIN
+
+int main() {
+    auto imu = readIMU("../imu_module/imu.csv");
+    auto lidar = readLidar("../lidar_module/lidar.csv");
+
+    State state;
+    state.x = VectorXd::Zero(15);
+    state.P = MatrixXd::Identity(15, 15) * 0.1;
+
+    ofstream traj("trajectory.csv");
+    traj << "timestamp,x,y,z,roll,pitch,yaw\n";
+
+    ofstream lidar_out("lidar_points.csv");
+    lidar_out << "x,y,z\n";
+
+    vector<Vector3d> prev_scan, curr_scan;
+
+    size_t lidar_idx = 0;
+
+    for (size_t i = 1;i < imu.size();i++) {
+
+        double dt = imu[i].t - imu[i - 1].t;
+        if (dt <= 0 || dt > 0.1) continue;
+
+        predict(state, imu[i], dt);
+
+        while (lidar_idx < lidar.size() &&
+            lidar[lidar_idx].t <= imu[i].t) {
+
+            curr_scan.push_back(lidarToWorld(state, lidar[lidar_idx]));
+            lidar_idx++;
+        }
+
+        if (curr_scan.size() > 200) {
+
+            if (!prev_scan.empty()) {
+
+                vector<Vector3d> src = downsample(curr_scan, 5);
+                vector<Vector3d> dst = downsample(prev_scan, 5);
+
+                Matrix3d R;
+                Vector3d t;
+
+                ICP_fast(src, dst, R, t);
+
+                state.x.segment<3>(0) += t;
+            }
+
+            prev_scan = curr_scan;
+            curr_scan.clear();
+        }
+
+        Vector3d pos = state.x.segment<3>(0);
+        Vector3d ang = state.x.segment<3>(6);
+
+        traj << imu[i].t << ","
+            << pos.x() << ","
+            << pos.y() << ","
+            << pos.z() << ","
+            << ang.x() << ","
+            << ang.y() << ","
+            << ang.z() << "\n";
+    }
+
+    for (auto& p : prev_scan) {
+        lidar_out << p.x() << ","
+            << p.y() << ","
+            << p.z() << "\n";
+    }
+
+    cout << "Done\n";
+    return 0;
+}

processing/visualization.py

@@ -0,0 +1,104 @@
+import open3d as o3d
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import os
+from datetime import datetime
+
+# СОЗДАНИЕ УНИКАЛЬНОЙ ПАПКИ
+
+timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+output_dir = f"results/run_{timestamp}"
+frames_dir = os.path.join(output_dir, "frames")
+
+os.makedirs(frames_dir, exist_ok=True)
+
+# ЗАГРУЗКА ДАННЫХ
+
+traj = pd.read_csv("trajectory.csv")
+lidar = pd.read_csv("lidar_points.csv")
+
+trajectory = traj[['x', 'y', 'z']].values
+angles = traj[['roll', 'pitch', 'yaw']].values
+lidar_points = lidar[['x', 'y', 'z']].values
+
+# СОХРАНЕНИЕ 2D ГРАФИКОВ
+
+plt.figure()
+plt.plot(trajectory[:,0], trajectory[:,1])
+plt.title("Trajectory (X-Y)")
+plt.xlabel("X")
+plt.ylabel("Y")
+plt.grid()
+plt.savefig(os.path.join(output_dir, "trajectory_plot.png"))
+plt.close()
+
+# OPEN3D
+
+pcd = o3d.geometry.PointCloud()
+pcd.points = o3d.utility.Vector3dVector(lidar_points)
+
+lines = [[i, i+1] for i in range(len(trajectory)-1)]
+line_set = o3d.geometry.LineSet()
+line_set.points = o3d.utility.Vector3dVector(trajectory)
+line_set.lines = o3d.utility.Vector2iVector(lines)
+
+vis = o3d.visualization.Visualizer()
+vis.create_window(visible=False)  # важно для сохранения кадров
+
+vis.add_geometry(pcd)
+vis.add_geometry(line_set)
+
+# ПОВОРОТ
+
+def get_rotation_matrix(roll, pitch, yaw):
+    Rx = np.array([
+        [1, 0, 0],
+        [0, np.cos(roll), -np.sin(roll)],
+        [0, np.sin(roll), np.cos(roll)]
+    ])
+    Ry = np.array([
+        [np.cos(pitch), 0, np.sin(pitch)],
+        [0, 1, 0],
+        [-np.sin(pitch), 0, np.cos(pitch)]
+    ])
+    Rz = np.array([
+        [np.cos(yaw), -np.sin(yaw), 0],
+        [np.sin(yaw), np.cos(yaw), 0],
+        [0, 0, 1]
+    ])
+    return Rz @ Ry @ Rx
+
+# СОХРАНЕНИЕ КАДРОВ
+
+for i in range(len(trajectory)):
+
+    frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.2)
+
+    pos = trajectory[i]
+    roll, pitch, yaw = angles[i]
+
+    R = get_rotation_matrix(roll, pitch, yaw)
+
+    frame.rotate(R, center=(0,0,0))
+    frame.translate(pos)
+
+    vis.clear_geometries()
+    vis.add_geometry(pcd)
+    vis.add_geometry(line_set)
+    vis.add_geometry(frame)
+
+    vis.poll_events()
+    vis.update_renderer()
+
+    # сохраняем изображение
+    img_path = os.path.join(frames_dir, f"frame_{i:04d}.png")
+    vis.capture_screen_image(img_path)
+
+# СОХРАНЕНИЕ ФИНАЛЬНОГО ВИДА
+
+vis.capture_screen_image(os.path.join(output_dir, "3d_view.png"))
+
+vis.destroy_window()
+
+print(f"Результаты сохранены в: {output_dir}")