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.
|Create occupancy grid with binary values|
|Create 2-D occupancy map|
|Create 3-D occupancy map|
|Collision-checking options between 3-D occupancy map and collision geometries|
|Create map layer for N-dimensional data|
|Manage multiple map layers|
|Discrete signed distance map of 2-D region|
|Build occupancy map from lidar scans|
|Check for collision between 3-D occupancy map and geometry|
|Check if locations are free or occupied|
|Export 3-D occupancy map as octree or binary tree file|
|Get occupancy probability of locations|
|Retrieve data from map layer|
|Import octree or binary tree file as 3-D occupancy map|
|Inflate each occupied location|
|Insert ray from laser scan observation|
|Insert 3-D points or point cloud observation into map|
|Generate map with randomly scattered obstacles|
|Generate random 2-D maze map|
|Move map in world frame|
|Convert occupancy map to matrix|
|Compute cell indices along a ray|
|Find intersection points of rays and occupied map cells|
|Set occupancy probability of locations|
|Assign data to map layer|
|Sync map with overlapping map|
|Update occupancy probability at locations|
- 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.