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 withMotion Planningto plan paths in a map, or useLocalization and Pose Estimationalgorithms 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 |
mapLayer |
Create map layer forN-dimensional data |
multiLayerMap |
Manage multiple map layers |
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 |
getMapData |
Retrieve data from map layer |
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 |
生成地图随机分散的障碍 |
mapMaze |
Generate random 2-D maze map |
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 |
setMapData |
Assign data to map layer |
syncWith |
Sync map with overlapping map |
show |
Show grid values in a figure |
updateOccupancy |
Integrate 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 theDriving 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 aslidarScan
objects and associated[x y theta]
poses to build anoccupancyMap
. - Fuse Multiple Lidar Sensors Using Map Layers
入住率地图提供了一个简单而强大的再保险方式presenting 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.