* @brief Get speeds and IMU biases for a given pose ID. * @param[in] bundle_id Bundle ID of the FrameBundle. * @param[out] speed_and_bias Speed

Returns duration of last measurement in seconds

CameraType value representing the camera model used by the derived class.

* @brief Get pose for a given pose ID. * @param[in] bundle_id Bundle ID of the FrameBundle. * @param[out] T_WS Homogeneous transformation of thi

Return state of constrain to axis mode.

Return state of constrain to axis mode.

Implementation of the least squares solution described in the paper: Robot Sensor Calibration: Solving AX=XB on the Euclidean Group by Frank C

Implementation of the least squares solution described in the paper: Robot Sensor Calibration: Solving AX=XB on the Euclidean Group by Frank C

Return unique point identifier.

Return 4x4 identity/unit matrix. >>> I = identity_matrix() >>> numpy.allclose(I, numpy.dot(I, I)) True >>> numpy.sum(I), numpy.trace(

Return 4x4 identity/unit matrix. >>> I = identity_matrix() >>> numpy.allclose(I, numpy.dot(I, I)) True >>> numpy.sum(I), numpy.trace(

These functions are called before and after monoCallback or stereoCallback. a derived class can implement some additional logic here.

\brief Get the IMU measurements.

Initialise pose from IMU measurements. For convenience as static.

Return value between 0 and 1 WARNING This function does not check whether the x/y is within the border

Return value between 0 and 255 WARNING This function does not check whether the x/y is within the border

Return inverse of square transformation matrix. >>> M0 = random_rotation_matrix() >>> M1 = inverse_matrix(M0.T) >>> numpy.allclose(M1, nu

Return inverse of square transformation matrix. >>> M0 = random_rotation_matrix() >>> M1 = inverse_matrix(M0.T) >>> numpy.allclose(M1, nu

Checks if a particular frame is still in the IMU window

* @brief Checks whether the landmark is added to the estimator. * @param landmark_id The ID. * @return True if added. */

Checks whether the landmark is initialized.

Info: is this parameter block connected to this marginalization error?

! This checks that the input is a pure rotation matrix 'm'. * The conditions for this are: R' * R = I and det(R) = 1 (proper rotation matrix) */

@brief Check if the state at a slot in states is a keyframe @param[in] slot index of frame in states_ vector \return true if the frame at slot is a ke

Check if memory is 8bit aligned.

Return True if two matrices perform same transformation. >>> is_same_transform(numpy.identity(4), numpy.identity(4)) True >>> is_same_tra

Return True if two matrices perform same transformation. >>> is_same_transform(numpy.identity(4), numpy.identity(4)) True >>> is_same_tra

Input must be already distorted px vector

Frame jacobian for projection of 3D point in (f)rame coordinate to unit plane coordinates uv (focal length = 1).

Jacobian of point projection on unit plane (focal length = 1) in frame (f).

Jacobian of point to unit bearing vector.