MATLAB Code Implementation for Creating 3D Maps

Creating 3D maps in MATLAB serves as a fundamental step for trajectory planning, terrain analysis, and simulation modeling. Through proper data processing and graphical rendering, intuitive 3D environmental models can be constructed, providing visual support for subsequent path planning algorithms.

### Core Approach Data Preparation 3D maps typically require terrain elevation data or point cloud data. MATLAB supports importing data from external files (such as DEM digital elevation models or CSV point cloud data) and can also generate simulated terrain using built-in functions. For example, the `peaks` function creates mountain-like surfaces for prototyping.

Coordinate Transformation and Grid Generation For discrete point data, use `meshgrid` to generate grid coordinates, then apply `griddata` interpolation to obtain uniformly distributed 3D points. For regularly structured data, surfaces can be directly plotted using `surf` or `mesh` functions.

Map Visualization Base Terrain: Render elevation surfaces using the `surf` function, applying `colormap` to differentiate heights through color gradients. Obstacle Integration: Draw obstacles like cubes or cylinders using `patch` or `fill3` functions, defining their positions and dimensions programmatically. Dynamic Elements: Overlay trajectory lines on the map with `plot3`, enabling real-time updates for moving objects.

Interaction Optimization Enable `rotate3d` to allow users to drag and rotate the viewport for enhanced interactivity. Improve depth perception by adjusting lighting (`light` function) and transparency settings (`alpha` properties).

### Extended Applications Trajectory Planning: Mark start/end points on the map and integrate pathfinding algorithms like A* or RRT for obstacle avoidance. Multi-Layer Overlays: Combine satellite imagery (using `geoshow`) or heatmaps to highlight specific regions within the 3D environment.

Leveraging MATLAB's graphics toolbox, developers can rapidly validate 3D environment feasibility, supporting algorithm testing in complex scenarios with integrated visualization capabilities.