Difference between revisions of "Example Visual Odometry Monocular Plane"
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Example Code: | Example Code: | ||
* [https://github.com/lessthanoptimal/BoofCV/blob/v0. | * [https://github.com/lessthanoptimal/BoofCV/blob/v0.38/examples/src/main/java/boofcv/examples/sfm/ExampleVisualOdometryMonocularPlane.java ExampleVisualOdometryMonocularPlane.java] | ||
Concepts: | Concepts: | ||
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FactoryVisualOdometry.monoPlaneInfinity(75, 2, 1.5, 200, tracker, ImageType.single(GrayU8.class)); | FactoryVisualOdometry.monoPlaneInfinity(75, 2, 1.5, 200, tracker, ImageType.single(GrayU8.class)); | ||
// Pass in intrinsic/extrinsic calibration. | // Pass in intrinsic/extrinsic calibration. This can be changed in the future. | ||
visualOdometry.setCalibration(calibration); | visualOdometry.setCalibration(calibration); | ||
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Vector3D_F64 T = leftToWorld.getT(); | Vector3D_F64 T = leftToWorld.getT(); | ||
System.out.printf("Location %8.2f %8.2f %8.2f | System.out.printf("Location %8.2f %8.2f %8.2f, %s\n", T.x, T.y, T.z, trackStats(visualOdometry)); | ||
} | } | ||
} | } | ||
} | } | ||
</syntaxhighlight> | </syntaxhighlight> |
Revision as of 11:53, 12 July 2021
This example demonstrates how to estimate the camera's ego motion using a single camera and known plane. Since the plane's relative location to the camera is known there is no scale ambiguity, like there is with a more general single camera solution.
Example Code:
Concepts:
- Plane/Homography
Related Examples:
Example Code
/**
* Bare bones example showing how to estimate the camera's ego-motion using a single camera and a known
* plane. Additional information on the scene can be optionally extracted from the algorithm,
* if it implements AccessPointTracks3D.
*
* @author Peter Abeles
*/
public class ExampleVisualOdometryMonocularPlane {
public static void main( String[] args ) {
MediaManager media = DefaultMediaManager.INSTANCE;
String directory = UtilIO.pathExample("vo/drc/");
// load camera description and the video sequence
MonoPlaneParameters calibration = CalibrationIO.load(
media.openFile(new File(directory, "mono_plane.yaml").getPath()));
SimpleImageSequence<GrayU8> video = media.openVideo(
new File(directory, "left.mjpeg").getPath(), ImageType.single(GrayU8.class));
// specify how the image features are going to be tracked
ConfigPKlt configKlt = new ConfigPKlt();
configKlt.pyramidLevels = ConfigDiscreteLevels.levels(4);
configKlt.templateRadius = 3;
ConfigPointDetector configDetector = new ConfigPointDetector();
configDetector.type = PointDetectorTypes.SHI_TOMASI;
configDetector.general.maxFeatures = 600;
configDetector.general.radius = 3;
configDetector.general.threshold = 1;
PointTracker<GrayU8> tracker = FactoryPointTracker.klt(configKlt, configDetector, GrayU8.class, null);
// declares the algorithm
MonocularPlaneVisualOdometry<GrayU8> visualOdometry =
FactoryVisualOdometry.monoPlaneInfinity(75, 2, 1.5, 200, tracker, ImageType.single(GrayU8.class));
// Pass in intrinsic/extrinsic calibration. This can be changed in the future.
visualOdometry.setCalibration(calibration);
// Process the video sequence and output the location plus number of inliers
while (video.hasNext()) {
GrayU8 image = video.next();
if (!visualOdometry.process(image)) {
System.out.println("Fault!");
visualOdometry.reset();
}
Se3_F64 leftToWorld = visualOdometry.getCameraToWorld();
Vector3D_F64 T = leftToWorld.getT();
System.out.printf("Location %8.2f %8.2f %8.2f, %s\n", T.x, T.y, T.z, trackStats(visualOdometry));
}
}
}