Occupancy maps are used to represent obstacles in an environment and define limits of your world. You can build maps and update obstacle locations from sensor readings using raycasting. Sync with existing maps and move local frames to create egocentric maps that follow your vehicle. Maps support binary and probabilistic values for 2-D maps and a probabilistic representation for 3-D maps.
2-D Occupancy Maps
3-D Occupancy Maps
|Create 3-D occupancy map (Since R2019b)
|Export 3-D occupancy map as octree or binary tree file (Since R2020a)
|Import octree or binary tree file as 3-D occupancy map (Since R2020a)
|Collision-checking options between 3-D occupancy map and collision geometries (Since R2022b)
Signed Distance Maps
- Occupancy Grids
Details of occupancy grid functionality and map structure.
- Fuse Multiple Lidar Sensors Using Map Layers
Occupancy maps offer a simple yet robust way of representing an environment for robotic applications by mapping the continuous world-space to a discrete data structure.
- Build Occupancy Map from Depth Images Using Visual Odometry and Optimized Pose Graph
This example shows how to reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-D pose graph optimization.