Mapping
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.
Use these maps along with Motion Planning to plan paths in a map, or use Localization and Pose Estimation algorithms to estimate your vehicle pose in an environment.
Objects
binaryOccupancyMap | Create occupancy grid with binary values |
occupancyMap | Create occupancy map with probabilistic values |
occupancyMap3D | Create 3-D occupancy map |
Functions
buildMap | Build occupancy map from lidar scans |
checkOccupancy | Check locations for free, occupied, or unknown values |
exportOccupancyMap3D | Import an octree file as 3D occupancy map |
getOccupancy | Get occupancy value of locations |
importOccupancyMap3D | Import an octree file as 3D occupancy map |
inflate | Inflate each occupied grid location |
insertRay | Insert ray from laser scan observation |
insertPointCloud | Insert 3-D points or point cloud observation into map |
mapClutter | Generate map with randomly scattered obstacles |
move | Move map in world frame |
occupancyMatrix | Convert occupancy grid to double matrix |
raycast | Compute cell indices along a ray |
rayIntersection | Find intersection points of rays and occupied map cells |
setOccupancy | Set occupancy value of locations |
syncWith | Sync map with overlapping map |
show | Show grid values in a figure |
updateOccupancy | Integrate probability observations at locations |
Topics
Details of occupancy grid functionality and map structure.
Create Egocentric Occupancy Maps Using Range Sensors
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.
Create Egocentric Occupancy Map from Driving Scenario Designer
This example shows how to create an egocentric occupancy map from the Driving Scenario Designer app.
Build Occupancy Map from Lidar Scans and Poses
The buildMap function takes in lidar scan readings and associated poses to build an occupancy grid as lidarScan objects and associated [x y theta] poses to build an occupancyMap.
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.
