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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

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

functions

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

topics


  • 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 reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-d pose graph optimization.

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