CpiV2 class
Model 2 of continuous preintegration.
Contents
This model is the "piecewise constant local acceleration assumption." Please see the following publication for details on the theory [13] :
Continuous Preintegration Theory for Graph-based Visual-Inertial Navigation Authors: Kevin Eckenhoff, Patrick Geneva, and Guoquan Huang http://udel.edu/~ghuang/papers/tr_
cpi.pdf
The steps to use this preintegration class are as follows:
- call setLinearizationPoints() to set the bias/orientation linearization point
- call feed_
IMU() will all IMU measurements you want to precompound over - access public varibles, to get means, Jacobians, and measurement covariance
Base classes
- class CpiBase
- Base class for continuous preintegration integrators.
Constructors, destructors, conversion operators
- CpiV2(double sigma_w, double sigma_wb, double sigma_a, double sigma_ab, bool imu_avg_ = false)
- Default constructor for our Model 2 preintegration (piecewise constant local acceleration assumption)
Public functions
- void feed_IMU(double t_0, double t_1, Eigen::Matrix<double, 3, 1> w_m_0, Eigen::Matrix<double, 3, 1> a_m_0, Eigen::Matrix<double, 3, 1> w_m_1 = Eigen::Matrix<double, 3, 1>::Zero(), Eigen::Matrix<double, 3, 1> a_m_1 = Eigen::Matrix<double, 3, 1>::Zero()) virtual
- Our precompound function for Model 2.
Public variables
- bool state_transition_jacobians
- If we want to use analytical jacobians or not. In the paper we just numerically integrated the jacobians If set to false, we use a closed form version similar to model 1.
Function documentation
ov_core::
Default constructor for our Model 2 preintegration (piecewise constant local acceleration assumption)
| Parameters | |
|---|---|
| sigma_w | gyroscope white noise density (rad/s/sqrt(hz)) |
| sigma_wb | gyroscope random walk (rad/s^2/sqrt(hz)) |
| sigma_a | accelerometer white noise density (m/s^2/sqrt(hz)) |
| sigma_ab | accelerometer random walk (m/s^3/sqrt(hz)) |
| imu_avg_ | if we want to average the imu measurements (IJRR paper did not do this) |
void ov_core::
Our precompound function for Model 2.
| Parameters | |
|---|---|
| t_0 in | first IMU timestamp |
| t_1 in | second IMU timestamp |
| w_m_0 in | first imu gyroscope measurement |
| a_m_0 in | first imu acceleration measurement |
| w_m_1 in | second imu gyroscope measurement |
| a_m_1 in | second imu acceleration measurement |
We will first analytically integrate our meansurement. We can numerically or analytically integrate our bias jacobians. Then we perform numerical integration for our measurement covariance.