占有グリッド

占有グリッドの機能とマップ構造体の詳細

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. Individual grid cells can contain binary or probabilistic information, where 0 indicates free-space, and 1 indicates occupied space. You can build up this information over time using sensor measurements and efficiently store them in the map. This information is also useful for more advanced workflows, such as collision detection and path planning.

Create Egocentric Occupancy Map from Driving Scenario Designer

This example shows how to create an egocentric occupancy map from the Driving Scenario Designer app. This example uses obstacle information from the vision detection generator to update the egocentric occupancy map.

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. In this example, you build an occupancy map from the depth images, which can be used for path planning while navigating in that environment.