Binary Occupancy Grid Implementation

.gitignore

@@ -37,5 +37,6 @@ src/simulations/perception/lidar_obstacle_sensing/__pycache__
 src/simulations/perception/sensor_auto_calibration/__pycache__
 test/__pycache__
 Test/__pycache__
+__pycache__
 .pytest_cache
 .coverage

src/components/mapping/grid/binary_occupancy_grid.py

@@ -0,0 +1,193 @@
+"""
+binary_occupancy_grid.py
+
+Author: Shantanu Parab
+"""
+
+import numpy as np
+import sys
+from pathlib import Path
+from collections import defaultdict
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+
+abs_dir_path = str(Path(__file__).absolute().parent)
+relative_path = "/../../../components/"
+
+
+sys.path.append(abs_dir_path + relative_path + "visualization")
+sys.path.append(abs_dir_path + relative_path + "obstacle")
+sys.path.append(abs_dir_path + relative_path + "state")
+
+
+from min_max import MinMax
+from obstacle_list import ObstacleList
+from obstacle import Obstacle
+from state import State
+import json
+
+
+# Define RGB colors for each grid value
+# Colors in the format [R, G, B], where values are in the range [0, 1]
+colors = [
+    [1.0, 1.0, 1.0],  # Free space (white)
+    [0.4, 0.8, 1.0],  # Explored nodes (light blue)
+    [0.0, 1.0, 0.0],  # Path (green)
+    [0.5, 0.5, 0.5],  # Clearance space (yellow-orange)
+    [0.0, 0.0, 0.0],  # Obstacles (red)
+]
+
+# Create a colormap
+custom_cmap = ListedColormap(colors)
+
+class BinaryOccupancyGrid:
+    def __init__(self, x_lim , y_lim, resolution, clearance, map_path):
+
+        self.x_min, self.x_max = x_lim.min_value(), x_lim.max_value()
+        self.y_min, self.y_max = y_lim.min_value(), y_lim.max_value()
+        self.resolution = resolution
+        self.clearance = clearance
+
+        self.map, self.x_range, self.y_range =  self.create_grid()
+        self.map_path = map_path
+
+
+    def create_grid(self):
+        """Create a grid based on the specified or derived limits."""
+
+        x_range = np.arange(self.x_min, self.x_max, self.resolution)
+        y_range = np.arange(self.y_min, self.y_max, self.resolution)
+
+        map = np.zeros((len(y_range), len(x_range)))  # Initialize map as free space
+
+        return map, x_range, y_range
+
+    def add_object(self, obtacle_list: ObstacleList):
+        """Mark obstacles and their clearance on the map, considering rotation (yaw)."""
+        for obs in obtacle_list.list:
+            # Get obstacle parameters
+            x_c = obs.state.x_m
+            y_c = obs.state.y_m
+            yaw = obs.state.yaw_rad
+            length = obs.length_m
+            width = obs.width_m
+
+            # Calculate the clearance dimensions
+            clearance_length = length + self.clearance
+            clearance_width = width + self.clearance
+
+            # Define corners for the clearance area
+            clearance_corners = np.array([
+                [-clearance_length, -clearance_width],
+                [-clearance_length, clearance_width],
+                [clearance_length, clearance_width],
+                [clearance_length, -clearance_width]
+            ])
+
+            # Define corners for the actual obstacle
+            obstacle_corners = np.array([
+                [-length, -width],
+                [-length, width],
+                [length, width],
+                [length, -width]
+            ])
+
+            # Apply rotation to both obstacle and clearance corners
+            rotation_matrix = np.array([
+                [np.cos(yaw), -np.sin(yaw)],
+                [np.sin(yaw), np.cos(yaw)]
+            ])
+            rotated_clearance_corners = np.dot(clearance_corners, rotation_matrix.T) + np.array([x_c, y_c])
+            rotated_obstacle_corners = np.dot(obstacle_corners, rotation_matrix.T) + np.array([x_c, y_c])
+
+            # Mark the clearance area
+            self._mark_area(rotated_clearance_corners, value=0.75)  # 0.5 for clearance
+
+            # Mark the actual obstacle area
+            self._mark_area(rotated_obstacle_corners, value=1.0)  # 1.0 for obstacles
+
+    def _point_in_polygon(self, x, y, corners):
+        """
+        Check if a point (x, y) is inside a polygon defined by corners.
+        Args:
+            x: X-coordinate of the point.
+            y: Y-coordinate of the point.
+            corners: Array of polygon corners in global coordinates.
+        Returns:
+            True if the point is inside the polygon, False otherwise.
+        """
+        n = len(corners)
+        inside = False
+        px, py = x, y
+        for i in range(n):
+            x1, y1 = corners[i]
+            x2, y2 = corners[(i + 1) % n]
+            if ((y1 > py) != (y2 > py)) and \
+            (px < (x2 - x1) * (py - y1) / (y2 - y1 + 1e-6) + x1):
+                inside = not inside
+        return inside
+
+
+    def _mark_area(self, corners, value):
+        """
+        Mark a rectangular area on the map based on the given rotated corners.
+        Args:
+            corners: The rotated corners of the area in global coordinates.
+            value: The value to mark in the map (e.g., 0.5 for clearance, 1.0 for obstacles).
+        """
+        # Get the bounding box of the corners
+        x_min = max(0, int((min(corners[:, 0]) - self.x_range[0]) / self.resolution))
+        x_max = min(self.map.shape[1], int((max(corners[:, 0]) - self.x_range[0]) / self.resolution))
+        y_min = max(0, int((min(corners[:, 1]) - self.y_range[0]) / self.resolution))
+        y_max = min(self.map.shape[0], int((max(corners[:, 1]) - self.y_range[0]) / self.resolution))
+
+        # Iterate through the map cells in the bounding box
+        for x in range(x_min, x_max):
+            for y in range(y_min, y_max):
+                # Get the center of the current cell
+                cell_x = self.x_range[0] + x * self.resolution + self.resolution / 2
+                cell_y = self.y_range[0] + y * self.resolution + self.resolution / 2
+
+                # Check if the cell center is inside the rotated polygon
+                if self._point_in_polygon(cell_x, cell_y, corners):
+                    self.map[y, x] = max(self.map[y, x], value)  # Mark the cell
+
+    # Save the map to a file as an image/json
+    def save_map(self):
+        """
+        Save the map to a file.
+        """
+
+        if self.map_path.endswith('.npy'):
+            np.save(self.map_path, self.map)
+        elif self.map_path.endswith('.png'):
+            plt.imsave(self.map_path, self.map, cmap=custom_cmap, origin='lower')
+        elif self.map_path.endswith('.json'):
+            map_list = self.map.tolist()
+            with open(self.map_path, 'w') as f:
+                json.dump(map_list, f)
+        else:
+            raise ValueError("Unsupported file format. Use .npy, .png, or .json")
+
+
+
+if  __name__ == "__main__":
+
+    obst_list = ObstacleList()
+    obst_list.add_obstacle(Obstacle(State(x_m=10.0, y_m=15.0), length_m=10, width_m=8))
+    obst_list.add_obstacle(Obstacle(State(x_m=40.0, y_m=0.0), length_m=2, width_m=10))
+    obst_list.add_obstacle(Obstacle(State(x_m=10.0, y_m=-10.0, yaw_rad=np.rad2deg(45)), length_m=5, width_m=5))
+    obst_list.add_obstacle(Obstacle(State(x_m=30.0, y_m=15.0, yaw_rad=np.rad2deg(10)), length_m=5, width_m=2))
+
+    bin_occ_grid = BinaryOccupancyGrid(MinMax(-5, 55), MinMax(-20, 25), 0.5, 1.5)
+    bin_occ_grid.add_object(obst_list)
+
+    bin_occ_grid.save_map("map.json")
+
+    plt.figure(figsize=(10, 8))
+    plt.imshow(bin_occ_grid.map, extent=[bin_occ_grid.x_range[0], bin_occ_grid.x_range[-1], bin_occ_grid.y_range[0], bin_occ_grid.y_range[-1]],
+            origin='lower', cmap=custom_cmap)
+
+    plt.legend()
+    plt.show()