Difference between revisions of "Example Stereo Visual Odometry"

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<center>
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<gallery widths=758px heights=384px >
<gallery widths=758px heights=384px >
File:Stereo_visual_odometry_screenshot.jpg|Screenshot from visual odometry appletExample just outputs estimated location.
File:Stereo_visual_odometry_screenshot.jpg|Screenshot from visual odometry demonstrationRed squares are camera locations and colorized dots are part of sparse reconstruction used to estimate motion.
</gallery>
</gallery>
</center>
</center>


Stereo visual odometry estimates the camera's egomotion using a pair of calibrated cameras.  Stereo camera systems are inheritly more stable than monocular ones because the stereo pair provides good triangulation of image features and resolves the scale ambiguity.  The example below shows how to use a high level interface with visual odometyr algorithms.  The basic high level interface hides internal data structures which are useful in many applications, which is why an optional interface is provided for accessing some of those structures.
Stereo visual odometry estimates the camera's egomotion using a pair of calibrated cameras.  Stereo camera systems are inherently more stable than monocular ones because the stereo pair provides good triangulation of image features and resolves the scale ambiguity.  The example below shows how to use a high level interface with visual odometry algorithms.  The basic high level interface hides internal data structures which are useful in many applications, which is why an optional interface is provided for accessing some of those structures.


Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.14/examples/src/boofcv/examples/ExampleStereoVisualOdometry.java ExampleStereoVisualOdometry.java]
Example File: [https://github.com/lessthanoptimal/BoofCV/blob/v0.36/examples/src/main/java/boofcv/examples/sfm/ExampleVisualOdometryStereo.java ExampleVisualOdometryStereo.java]


Concepts:
Concepts:
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* Feature Tracking
* Feature Tracking


Relevant Applets:
Relevant Videos:
* [[Applet_Feature_Tracking| Feature Tracking]]
* [https://www.youtube.com/watch?v=8pn9Ebw90uk&t=292s Video 2020]
* [[Applet_Stereo_Visual_Odometry| Stereo Visual Odometry]]
* [https://www.youtube.com/watch?v=D4I4NHSGaOc Video 2013]
** [http://www.youtube.com/watch?v=D4I4NHSGaOc Video]


Related Tutorials/Example Code:  
Related Tutorials/Example Code:  
* [[Tutorial_Geometric_Vision| Tutorial Geometric Vision]]
* [[Tutorial_Geometric_Vision| Tutorial Geometric Vision]]
* [[Example Visual Odometry Monocular Plane| Visual Odometry Monocular Plane]]
* [[Example Visual Odometry Depth| Visual Odometry Depth]]
= Example Code =


<syntaxhighlight lang="java">
<syntaxhighlight lang="java">
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  * @author Peter Abeles
  * @author Peter Abeles
  */
  */
public class ExampleStereoVisualOdometry {
public class ExampleVisualOdometryStereo {


public static void main( String args[] ) {
public static void main( String args[] ) {
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MediaManager media = DefaultMediaManager.INSTANCE;
MediaManager media = DefaultMediaManager.INSTANCE;


String directory = "../data/applet/vo/backyard/";
String directory = UtilIO.pathExample("vo/backyard/");


// load camera description and the video sequence
// load camera description and the video sequence
StereoParameters stereoParam = BoofMiscOps.loadXML(media.openFile(directory+"stereo.xml"));
StereoParameters stereoParam = CalibrationIO.load(media.openFile(new File(directory , "stereo.yaml").getPath()));
SimpleImageSequence<ImageUInt8> video1 = media.openVideo(directory + "left.mjpeg", ImageDataType.single(ImageUInt8.class));
SimpleImageSequence<GrayU8> video1 = media.openVideo(
SimpleImageSequence<ImageUInt8> video2 = media.openVideo(directory+"right.mjpeg",ImageDataType.single(ImageUInt8.class));
new File(directory , "left.mjpeg").getPath(), ImageType.single(GrayU8.class));
SimpleImageSequence<GrayU8> video2 = media.openVideo(
new File(directory,"right.mjpeg").getPath(), ImageType.single(GrayU8.class));
 
// Specify which tracker and how it will behave
var configKlt = new ConfigPKlt();
configKlt.pyramidLevels = ConfigDiscreteLevels.levels(4);
configKlt.templateRadius = 4;
configKlt.toleranceFB = 3;
configKlt.pruneClose = true;
 
var configDet = new ConfigPointDetector();
configDet.type = PointDetectorTypes.SHI_TOMASI;
configDet.shiTomasi.radius = 4;
configDet.general.maxFeatures = 300;
configDet.general.radius = 5;


// specify how the image features are going to be tracked
// We will estimate the location of features using block matching stereo
PkltConfig<ImageUInt8, ImageSInt16> configKlt = PkltConfig.createDefault(ImageUInt8.class, ImageSInt16.class);
var configBM = new ConfigDisparityBM();
configKlt.pyramidScaling = new int[]{1, 2, 4, 8};
configBM.errorType = DisparityError.CENSUS;
configKlt.templateRadius = 3;
configBM.disparityMin = 0;
configBM.disparityRange = 50;
configBM.regionRadiusX = 3;
configBM.regionRadiusY = 3;
configBM.maxPerPixelError = 30;
configBM.texture = 0.05;
configBM.validateRtoL = 1;
configBM.subpixel = true;


PointTrackerTwoPass<ImageUInt8> tracker =
// Configurations related to how the structure is chained together frame to frame
FactoryPointTrackerTwoPass.klt(configKlt, new ConfigGeneralDetector(600, 3, 1));
var configVisOdom = new ConfigVisOdomTrackPnP();
configVisOdom.keyframes.geoMinCoverage = 0.4;
configVisOdom.ransac.iterations = 200;
configVisOdom.ransac.inlierThreshold = 1.0;


// computes the depth of each point
// Declare each component then visual odometry
StereoDisparitySparse<ImageUInt8> disparity =
PointTracker<GrayU8> tracker = FactoryPointTracker.klt(configKlt, configDet,GrayU8.class, GrayS16.class);
FactoryStereoDisparity.regionSparseWta(0, 150, 3, 3, 30, -1, true, ImageUInt8.class);
StereoDisparitySparse<GrayU8> disparity = FactoryStereoDisparity.sparseRectifiedBM(configBM, GrayU8.class);
StereoVisualOdometry<GrayU8> visodom = FactoryVisualOdometry.stereoMonoPnP(configVisOdom,disparity,tracker, GrayU8.class);


// declares the algorithm
// Optionally dump verbose debugging information to stdout
StereoVisualOdometry<ImageUInt8> visualOdometry = FactoryVisualOdometry.stereoDepth(1.5,120, 2,200,50,true,
// Set<String> configuration = new HashSet<>();
disparity, tracker, ImageUInt8.class);
// configuration.add(VisualOdometry.VERBOSE_RUNTIME);
// configuration.add(VisualOdometry.VERBOSE_TRACKING);
// visodom.setVerbose(System.out,configuration);


// Pass in intrinsic/extrinsic calibration.  This can be changed in the future.
// Pass in intrinsic/extrinsic calibration.  This can be changed in the future.
visualOdometry.setCalibration(stereoParam);
visodom.setCalibration(stereoParam);


// Process the video sequence and output the location plus number of inliers
// Process the video sequence and output the location plus number of inliers
long startTime = System.nanoTime();
while( video1.hasNext() ) {
while( video1.hasNext() ) {
ImageUInt8 left = video1.next();
GrayU8 left = video1.next();
ImageUInt8 right = video2.next();
GrayU8 right = video2.next();


if( !visualOdometry.process(left,right) ) {
if( !visodom.process(left,right) ) {
throw new RuntimeException("VO Failed!");
throw new RuntimeException("VO Failed!");
}
}


Se3_F64 leftToWorld = visualOdometry.getLeftToWorld();
Se3_F64 leftToWorld = visodom.getCameraToWorld();
Vector3D_F64 T = leftToWorld.getT();
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));
System.out.printf("Location %8.2f %8.2f %8.2f   %s\n", T.x, T.y, T.z, trackStats(visodom));
}
}
System.out.printf("FPS %4.2f\n", video1.getFrameNumber()/((System.nanoTime()-startTime)*1e-9));
}
}


/**
/**
* If the algorithm implements AccessPointTracks3D, then count the number of inlier features
* If the algorithm implements AccessPointTracks3D create a string which summarizing different tracking information
* and return a string.
*/
*/
public static String inlierPercent(StereoVisualOdometry alg) {
public static String trackStats(StereoVisualOdometry alg) {
if( !(alg instanceof AccessPointTracks3D))
if( !(alg instanceof AccessPointTracks3D))
return "";
return "";
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int count = 0;
int count = 0;
int N = access.getAllTracks().size();
int N = access.getTotalTracks();
int totalNew = 0;
for( int i = 0; i < N; i++ ) {
for( int i = 0; i < N; i++ ) {
if( access.isInlier(i) )
if( access.isTrackInlier(i) )
count++;
count++;
else if( access.isTrackNew(i) ) {
totalNew++;
}
}
}


return String.format("%%%5.3f", 100.0 * count / N);
return String.format("inlier %5.1f%% new %4d total %d", 100.0 * count / (N-totalNew),totalNew,N);
}
}
}
}
</syntaxhighlight>
</syntaxhighlight>

Revision as of 21:17, 21 June 2020

Stereo visual odometry estimates the camera's egomotion using a pair of calibrated cameras. Stereo camera systems are inherently more stable than monocular ones because the stereo pair provides good triangulation of image features and resolves the scale ambiguity. The example below shows how to use a high level interface with visual odometry algorithms. The basic high level interface hides internal data structures which are useful in many applications, which is why an optional interface is provided for accessing some of those structures.

Example File: ExampleVisualOdometryStereo.java

Concepts:

  • Structure from Motion
  • Geometric Vision
  • Feature Tracking

Relevant Videos:

Related Tutorials/Example Code:

Example Code

/**
 * Bare bones example showing how to estimate the camera's ego-motion using a stereo camera system. Additional
 * information on the scene can be optionally extracted from the algorithm if it implements AccessPointTracks3D.
 *
 * @author Peter Abeles
 */
public class ExampleVisualOdometryStereo {

	public static void main( String args[] ) {

		MediaManager media = DefaultMediaManager.INSTANCE;

		String directory = UtilIO.pathExample("vo/backyard/");

		// load camera description and the video sequence
		StereoParameters stereoParam = CalibrationIO.load(media.openFile(new File(directory , "stereo.yaml").getPath()));
		SimpleImageSequence<GrayU8> video1 = media.openVideo(
				new File(directory , "left.mjpeg").getPath(), ImageType.single(GrayU8.class));
		SimpleImageSequence<GrayU8> video2 = media.openVideo(
				new File(directory,"right.mjpeg").getPath(), ImageType.single(GrayU8.class));

		// Specify which tracker and how it will behave
		var configKlt = new ConfigPKlt();
		configKlt.pyramidLevels = ConfigDiscreteLevels.levels(4);
		configKlt.templateRadius = 4;
		configKlt.toleranceFB = 3;
		configKlt.pruneClose = true;

		var configDet = new ConfigPointDetector();
		configDet.type = PointDetectorTypes.SHI_TOMASI;
		configDet.shiTomasi.radius = 4;
		configDet.general.maxFeatures = 300;
		configDet.general.radius = 5;

		// We will estimate the location of features using block matching stereo
		var configBM = new ConfigDisparityBM();
		configBM.errorType = DisparityError.CENSUS;
		configBM.disparityMin = 0;
		configBM.disparityRange = 50;
		configBM.regionRadiusX = 3;
		configBM.regionRadiusY = 3;
		configBM.maxPerPixelError = 30;
		configBM.texture = 0.05;
		configBM.validateRtoL = 1;
		configBM.subpixel = true;

		// Configurations related to how the structure is chained together frame to frame
		var configVisOdom = new ConfigVisOdomTrackPnP();
		configVisOdom.keyframes.geoMinCoverage = 0.4;
		configVisOdom.ransac.iterations = 200;
		configVisOdom.ransac.inlierThreshold = 1.0;

		// Declare each component then visual odometry
		PointTracker<GrayU8> tracker = FactoryPointTracker.klt(configKlt, configDet,GrayU8.class, GrayS16.class);
		StereoDisparitySparse<GrayU8> disparity = FactoryStereoDisparity.sparseRectifiedBM(configBM, GrayU8.class);
		StereoVisualOdometry<GrayU8> visodom = FactoryVisualOdometry.stereoMonoPnP(configVisOdom,disparity,tracker, GrayU8.class);

		// Optionally dump verbose debugging information to stdout
//		Set<String> configuration = new HashSet<>();
//		configuration.add(VisualOdometry.VERBOSE_RUNTIME);
//		configuration.add(VisualOdometry.VERBOSE_TRACKING);
//		visodom.setVerbose(System.out,configuration);

		// Pass in intrinsic/extrinsic calibration.  This can be changed in the future.
		visodom.setCalibration(stereoParam);

		// Process the video sequence and output the location plus number of inliers
		long startTime = System.nanoTime();
		while( video1.hasNext() ) {
			GrayU8 left = video1.next();
			GrayU8 right = video2.next();

			if( !visodom.process(left,right) ) {
				throw new RuntimeException("VO Failed!");
			}

			Se3_F64 leftToWorld = visodom.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(visodom));
		}
		System.out.printf("FPS %4.2f\n", video1.getFrameNumber()/((System.nanoTime()-startTime)*1e-9));
	}

	/**
	 * If the algorithm implements AccessPointTracks3D create a string which summarizing different tracking information
	 */
	public static String trackStats(StereoVisualOdometry alg) {
		if( !(alg instanceof AccessPointTracks3D))
			return "";

		AccessPointTracks3D access = (AccessPointTracks3D)alg;

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

		return String.format("inlier %5.1f%% new %4d total %d", 100.0 * count / (N-totalNew),totalNew,N);
	}
}