Difference between revisions of "Example Visual Odometry Monocular Plane"

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Example Code:
Example Code:
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/sfm/ExampleVisualOdometryMonocularPlane.java ExampleVisualOdometryMonocularPlane.java]
* [https://github.com/lessthanoptimal/BoofCV/blob/v0.37/examples/src/main/java/boofcv/examples/sfm/ExampleVisualOdometryMonocularPlane.java ExampleVisualOdometryMonocularPlane.java]


Concepts:
Concepts:
Line 23: Line 23:
  */
  */
public class ExampleVisualOdometryMonocularPlane {
public class ExampleVisualOdometryMonocularPlane {
 
public static void main( String[] args ) {
public static void main( String args[] ) {
 
MediaManager media = DefaultMediaManager.INSTANCE;
MediaManager media = DefaultMediaManager.INSTANCE;


Line 32: Line 30:
// load camera description and the video sequence
// load camera description and the video sequence
MonoPlaneParameters calibration = CalibrationIO.load(
MonoPlaneParameters calibration = CalibrationIO.load(
media.openFile(new File(directory , "mono_plane.yaml").getPath()));
media.openFile(new File(directory, "mono_plane.yaml").getPath()));
SimpleImageSequence<GrayU8> video = media.openVideo(
SimpleImageSequence<GrayU8> video = media.openVideo(
new File(directory , "left.mjpeg").getPath(), ImageType.single(GrayU8.class));
new File(directory, "left.mjpeg").getPath(), ImageType.single(GrayU8.class));


// specify how the image features are going to be tracked
// specify how the image features are going to be tracked
Line 56: Line 54:


// Process the video sequence and output the location plus number of inliers
// Process the video sequence and output the location plus number of inliers
while( video.hasNext() ) {
while (video.hasNext()) {
GrayU8 image = video.next();
GrayU8 image = video.next();


if( !visualOdometry.process(image) ) {
if (!visualOdometry.process(image)) {
System.out.println("Fault!");
System.out.println("Fault!");
visualOdometry.reset();
visualOdometry.reset();
Line 75: Line 73:
* and return a string.
* and return a string.
*/
*/
public static String inlierPercent(VisualOdometry<?> alg) {
public static String inlierPercent( VisualOdometry<?> alg ) {
if( !(alg instanceof AccessPointTracks3D))
if (!(alg instanceof AccessPointTracks3D))
return "";
return "";


Line 83: Line 81:
int count = 0;
int count = 0;
int N = access.getTotalTracks();
int N = access.getTotalTracks();
for( int i = 0; i < N; i++ ) {
for (int i = 0; i < N; i++) {
if( access.isTrackInlier(i) )
if (access.isTrackInlier(i))
count++;
count++;
}
}


return String.format("%%%5.3f", 100.0 * count / N);
return String.format("%%%5.3f", 100.0*count/N);
}
}
}
}
</syntaxhighlight>
</syntaxhighlight>

Revision as of 19:44, 21 December 2020

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      inliers %s\n", T.x, T.y, T.z, inlierPercent(visualOdometry));
		}
	}

	/**
	 * If the algorithm implements AccessPointTracks3D, then count the number of inlier features
	 * and return a string.
	 */
	public static String inlierPercent( VisualOdometry<?> alg ) {
		if (!(alg instanceof AccessPointTracks3D))
			return "";

		AccessPointTracks3D access = (AccessPointTracks3D)alg;

		int count = 0;
		int N = access.getTotalTracks();
		for (int i = 0; i < N; i++) {
			if (access.isTrackInlier(i))
				count++;
		}

		return String.format("%%%5.3f", 100.0*count/N);
	}
}