Mapping

2-D and 3-D occupancy maps, egocentric maps, raycasting

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

binaryOccupancyMapCreate occupancy grid with binary values
occupancyMapCreate occupancy map with probabilistic values
occupancyMap3D Create 3-D occupancy map

Functions

buildMapBuild occupancy map from lidar scans
checkOccupancyCheck locations for free, occupied, or unknown values
getOccupancyGet occupancy value of locations
inflateInflate each occupied grid location
insertRayInsert ray from laser scan observation
insertPointCloudInsert 3-D points or point cloud observation into map
moveMove map in world frame
occupancyMatrixConvert occupancy grid to double matrix
raycastCompute cell indices along a ray
rayIntersectionFind intersection points of rays and occupied map cells
setOccupancySet occupancy value of locations
syncWithSync map with overlapping map
showShow grid values in a figure
updateOccupancyIntegrate probability observations at locations

Topics

Occupancy Grids

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.

Featured Examples