Binary Occupancy Grid Implementation

[shantanuparabumd]

PR Description: Binary Occupancy Grid Implementation

Overview

This PR introduces a BinaryOccupancyGrid class that provides a grid-based representation of the environment for autonomous vehicle path planning and simulation. The implementation supports obstacle modeling, clearance handling, and visualization, making it a robust foundational tool for tasks like path planning, collision avoidance, and trajectory generation.

Key Features

1. Grid-Based Representation

• The grid is created based on user-defined limits (x_lim, y_lim) and resolution.
• Each cell in the grid represents a specific region of the map, allowing for spatial discretization.
• Resolution determines the granularity of the grid: higher resolution captures finer details but requires more memory.

2. Obstacle Handling

• Supports adding obstacles with attributes such as position (x, y), dimensions (length, width), and rotation (yaw).
• Obstacles are represented in the grid with values corresponding to their presence.

3. Clearance Space

• Clearance is calculated as a combination of obstacle clearance and an additional buffer to ensure safe navigation.
• Clearance is marked distinctly on the grid for visualization purposes, providing a clear separation between free space, clearance zones, and obstacles.

4. Visualization

• Utilizes a custom colormap to visually distinguish:
  • Free space: White.
  • Explored nodes: Light blue.
  • Path: Green (if integrated with a path planner).
  • Clearance zones: Gray.
  • Obstacles: Black.
• Users can save the grid as an image (.png), JSON (.json), or binary NumPy file (.npy).

5. Obstacle Modeling

• Obstacles are defined using corner-based calculations and transformations to account for rotation (yaw).
• Efficient marking of grid cells within obstacle and clearance boundaries using polygon-based collision checks.

6. Map Saving

• The grid map can be saved in multiple formats:
  • PNG: For visualization.
  • Numpy Binary (.npy): For programmatic reloading.
  • JSON: For sharing or debugging.

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Usage Instructions

Input

1. Map Configuration:

   • Provide x_lim and y_lim to specify map boundaries.
   • Set the grid resolution to control cell size.
   • Specify a clearance value for obstacle and vehicle safety.

2. Obstacles:

   • Define obstacles with attributes like position, length, width, and yaw.
   • Add obstacles using an ObstacleList object.

Example

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))

bin_occ_grid = BinaryOccupancyGrid(MinMax(-5, 55), MinMax(-20, 25), resolution=0.5, clearance=1.5)
bin_occ_grid.add_object(obst_list)
bin_occ_grid.save_map("map.json")

Output

• A saved grid map in the specified format (e.g., JSON, PNG).
• Visualized grid showing free space, obstacles, and clearance zones.

[ShisatoYano]

This PR can not be accepted because the previous commits for A star path planning implementation are also included. You need to recreate a new PR and it should include changes only for Binary Occupancy Grid Map implementation.