Hi Luan,
It is great to know that you are working on developing a simulation for autonomous parking. It is possible to use ultrasonic data for creating a cost map in a driving scenario, but it may not be the most suitable approach for your specific use case. Ultrasonic sensors are commonly used for short-range obstacle detection and are not typically used for SLAM (Simultaneous Localization and Mapping) algorithms.
SLAM algorithms, such as those implemented in MATLAB, are commonly designed to work with lidar data, which provides more accurate and detailed 3D point cloud information. Lidar sensors are better suited for creating a precise representation of the environment, which is crucial for motion planning in autonomous systems.
To create a cost map, you typically need more comprehensive data about the environment, including information about obstacles' size, shape, and position. Lidar sensors are better suited for capturing this level of detail. Therefore, it may be more appropriate to consider using lidar data or a combination of lidar and ultrasonic data to generate a cost map for your motion planning function.
If lidar data is not available or not feasible, you could explore alternative approaches such as using camera-based perception algorithms or depth sensors like time-of-flight cameras. These sensors can provide more detailed information about the environment, allowing you to create a more accurate cost map for motion planning.
Hope you find it helpful.
