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.
|Build occupancy map from lidar scans|
|Check locations for free, occupied, or unknown values|
|Get occupancy value of locations|
|Inflate each occupied grid location|
|Insert ray from laser scan observation|
|Insert 3-D points or point cloud observation into map|
|Move map in world frame|
|Convert occupancy grid to double matrix|
|Compute cell indices along a ray|
|Find intersection points of rays and occupied map cells|
|Set occupancy value of locations|
|Sync map with overlapping map|
|Show grid values in a figure|
|Integrate probability observations at locations|
Details of occupancy grid functionality and map structure.
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.
This example shows how to create an egocentric occupancy map from the Driving Scenario Designer app.
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
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.