|Build occupancy map from lidar scans|
|Check locations for free, occupied, or unknown values|
|八分木ファイルを 3 次元占有マップとしてインポート|
|Get occupancy value of locations|
|Retrieve data from map layer|
|Import an octree file as 3D occupancy map|
|Inflate each occupied grid 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 grid to double matrix|
|Compute cell indices along a ray|
|Find intersection points of rays and occupied map cells|
|Set occupancy value of locations|
|Assign data to map layer|
|Sync map with overlapping map|
|Show grid values in a figure|
|Integrate probability observations at locations|
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.
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.
lidarScanオブジェクトとして作成するために LIDAR スキャンの読み取り値と関連付けられている姿勢を受け取り、
[x y theta] を受け取ります。
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 about obstacle information. However, an autonomous platform may use a variety of sensors that may need to be combined to estimate both the current state of the platform and the state of the surrounding environment.
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.