read

Return target data using lidar

Since R2024b

Syntax

[pointCloud,range,reflectivity,semantic] = read(lidar)

Description

[pointCloud,range,reflectivity,semantic] = read(lidar) returns a point cloud, distances from the sensor to object points, reflectivity of surface materials, and the semantic identifiers of the objects in a scene. You can set the field of view and angular resolution in the sim3d.sensors.Lidar object specified by lidar.

Input Arguments

lidar — Virtual lidar sensor that detects target

sim3d.sensors.Lidar object

Virtual lidar sensor that detects target in the 3D environment, specified as a sim3d.sensors.Lidar object.

Example: lidar = sim3d.sensors.Lidar

Output Arguments

pointCloud — Point cloud data

m-by-n-by-3 array of positive real-valued [x, y, z] points

Point cloud data, returned as an m-by-n-by 3 array of positive, real-valued [x, y, z] points. m and n define the number of points in the point cloud, as shown in this equation:

$$m\times n=\frac{V_{FOV}}{V_{RES}}\times \frac{H_{FOV}}{H_{RES}}$$

where:

• V_FOV is the vertical field of view of the lidar, in degrees, as specified by the VerticalFieldOfView argument.

• V_RES is the vertical angular resolution of the lidar, in degrees, as specified by the VerticalAngularResolution argument.

• H_FOV is the horizontal field of view of the lidar, in degrees, as specified by the HorizontalFieldOfView argument.

• H_RES is the horizontal angular resolution of the lidar, in degrees, as specified by the HorizontalAngularResolution argument.

Each m-by-n entry in the array specifies the x, y, and z coordinates of a detected point in the sensor coordinate system. If the lidar does not detect a point at a given coordinate, then x, y, and z are returned as NaN.

You can create a point cloud from these returned points by using point cloud functions in the pointCloud (Computer Vision Toolbox) object.

Data Types: single

range — Distance to object points

m-by-n positive real-valued matrix

Distance to object points measured by the lidar sensor, returned as an m-by-n positive real-valued matrix. Each m-by-n value in the matrix corresponds to an [x, y, z] coordinate point returned by the pointCloud output argument.

Data Types: single

reflectivity — Reflectivity of surface materials

m-by-n matrix of intensity values in range [0, 1]

Reflectivity of surface materials, returned as an m-by-n matrix of intensity values in the range [0, 1], where m is the number of rows in the point cloud and n is the number of columns. Each point in the reflectivity output corresponds to a point in the pointCloud output. The function returns points that are not part of a surface material as NaN.

To calculate reflectivity, the lidar sensor uses the Phong reflection model [1]. The model describes surface reflectivity as a combination of diffuse reflections (scattered reflections, such as from rough surfaces) and specular reflections (mirror-like reflections, such as from smooth surfaces).

Data Types: single

semantic — Semantic label identifier

m-by-n-by-3 array of RGB triplet values

Semantic label identifier for each pixel in the image, output as an m-by-n array of RGB triplet values. m is the vertical resolution of the image. n is the horizontal resolution of the image.

The table shows the object IDs used in the default scenes. If a scene contains an object that does not have an assigned ID, that object is assigned an ID of 0. The detection of lane markings is not supported.

ID	Type
0	None/default
1	Building
2	Not used
3	Other
4	Pedestrians
5	Pole
6	Lane Markings
7	Road
8	Sidewalk
9	Vegetation
10	Vehicle
11	Not used
12	Generic traffic sign
13	Stop sign
14	Yield sign
15	Speed limit sign
16	Weight limit sign
17-18	Not used
19	Left and right arrow warning sign
20	Left chevron warning sign
21	Right chevron warning sign
22	Not used
23	Right one-way sign
24	Not used
25	School bus only sign
26-38	Not used
39	Crosswalk sign
40	Not used
41	Traffic signal
42	Curve right warning sign
43	Curve left warning sign
44	Up right arrow warning sign
45-47	Not used
48	Railroad crossing sign
49	Street sign
50	Roundabout warning sign
51	Fire hydrant
52	Exit sign
53	Bike lane sign
54-56	Not used
57	Sky
58	Curb
59	Flyover ramp
60	Road guard rail
61	Bicyclist
62-66	Not used
67	Deer
68-70	Not used
71	Barricade
72	Motorcycle
73-255	Not used

Dependencies

To enable semantic output, set the EnableSemanticOutput argument of the sim3d.sensors.Camera object to 1.

References

[1] Phong, Bui Tuong. “Illumination for Computer Generated Pictures.” Communications of the ACM 18, no. 6 (June 1975): 311–17. https://doi.org/10.1145/360825.360839.

Version History

Introduced in R2024b