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Added reference, made documentation consistent

release/4.3a0
dellaert 2015-07-20 20:57:47 -07:00
parent 6496bd49ed
commit 814c170caa
2 changed files with 46 additions and 35 deletions
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39
gtsam/navigation/CombinedImuFactor.h
View File

@ -28,6 +28,25 @@
namespace gtsam {
/*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating
* conditionally independent sets in factor graphs: a unifying perspective based
* on smart factors, Int. Conf. on Robotics and Automation (ICRA), 2014.
*
* REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
* Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for
* High-Dynamic Motion in Built Environments Without Initial Conditions",
* TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
* Computation of the Jacobian Matrices", Tech. Report, 2013.
* [4] C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, IMU Preintegration on
* Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation,
* Robotics: Science and Systems (RSS), 2015.
*/
/**
* PreintegratedCombinedMeasurements integrates the IMU measurements
* (rotation rates and accelerations) and the corresponding covariance matrix.
@ -35,6 +54,8 @@ namespace gtsam {
* is done incrementally (ideally, one integrates the measurement as soon as
* it is received from the IMU) so as to avoid costly integration at time of
* factor construction.
*
* @addtogroup SLAM
*/
class PreintegratedCombinedMeasurements : public PreintegrationBase {
@ -131,22 +152,6 @@ class PreintegratedCombinedMeasurements : public PreintegrationBase {
};
/**
* @addtogroup SLAM
*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating
* conditionally independent sets in factor graphs: a unifying perspective based
* on smart factors, Int. Conf. on Robotics and Automation (ICRA), 2014.
*
** REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
* Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for
* High-Dynamic Motion in Built Environments Without Initial Conditions",
* TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
* Computation of the Jacobian Matrices", Tech. Report, 2013.
*
* CombinedImuFactor is a 6-ways factor involving previous state (pose and
* velocity of the vehicle, as well as bias at previous time step), and current
* state (pose, velocity, bias at current time step). Following the pre-
@ -162,6 +167,8 @@ class PreintegratedCombinedMeasurements : public PreintegrationBase {
* 3) The covariance matrix of the PreintegratedCombinedMeasurements preserves
* the correlation between the bias uncertainty and the preintegrated
* measurements uncertainty.
*
* @addtogroup SLAM
*/
class CombinedImuFactor: public NoiseModelFactor6<Pose3, Vector3, Pose3,
Vector3, imuBias::ConstantBias, imuBias::ConstantBias> {

42
gtsam/navigation/ImuFactor.h
View File

@ -28,6 +28,25 @@
namespace gtsam {
/*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating
* conditionally independent sets in factor graphs: a unifying perspective based
* on smart factors, Int. Conf. on Robotics and Automation (ICRA), 2014.
*
* REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
* Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for
* High-Dynamic Motion in Built Environments Without Initial Conditions",
* TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
* Computation of the Jacobian Matrices", Tech. Report, 2013.
* [4] C. Forster, L. Carlone, F. Dellaert, D. Scaramuzza, IMU Preintegration on
* Manifold for Efficient Visual-Inertial Maximum-a-Posteriori Estimation,
* Robotics: Science and Systems (RSS), 2015.
*/
/**
* PreintegratedIMUMeasurements accumulates (integrates) the IMU measurements
* (rotation rates and accelerations) and the corresponding covariance matrix.
@ -35,6 +54,8 @@ namespace gtsam {
* Integration is done incrementally (ideally, one integrates the measurement
* as soon as it is received from the IMU) so as to avoid costly integration
* at time of factor construction.
*
* @addtogroup SLAM
*/
class PreintegratedImuMeasurements: public PreintegrationBase {
@ -104,25 +125,6 @@ private:
}
};
/**
*
* @addtogroup SLAM
*
* If you are using the factor, please cite:
* L. Carlone, Z. Kira, C. Beall, V. Indelman, F. Dellaert, Eliminating conditionally
* independent sets in factor graphs: a unifying perspective based on smart factors,
* Int. Conf. on Robotics and Automation (ICRA), 2014.
*
** REFERENCES:
* [1] G.S. Chirikjian, "Stochastic Models, Information Theory, and Lie Groups",
* Volume 2, 2008.
* [2] T. Lupton and S.Sukkarieh, "Visual-Inertial-Aided Navigation for
* High-Dynamic Motion in Built Environments Without Initial Conditions",
* TRO, 28(1):61-76, 2012.
* [3] L. Carlone, S. Williams, R. Roberts, "Preintegrated IMU factor:
* Computation of the Jacobian Matrices", Tech. Report, 2013.
*/
/**
* ImuFactor is a 5-ways factor involving previous state (pose and velocity of
* the vehicle at previous time step), current state (pose and velocity at
@ -132,6 +134,8 @@ private:
* Note that this factor does not model "temporal consistency" of the biases
* (which are usually slowly varying quantities), which is up to the caller.
* See also CombinedImuFactor for a class that does this for you.
*
* @addtogroup SLAM
*/
class ImuFactor: public NoiseModelFactor5<Pose3, Vector3, Pose3, Vector3,
imuBias::ConstantBias> {