Closed RuPingCen closed 2 years ago
The documentation should explain what exactly we perform, please take a look: https://docs.openvins.com/classov__msckf_1_1StateHelper.html#ade3a86a89f15cc6e03a6430fd1dee694
After propagation, normally we augment the state with an new clone that is at the new update timestep. This augmentation clones the IMU pose and adds it to our state's clone map. If we are doing time offset calibration we also make our cloning a function of the time offset.
I hope these issues also help you:
You can additionally refer to the paper state definition (eq (3)).
http://udel.edu/~pgeneva/downloads/papers/c10.pdf
Thank you for your patient guidance. I guess I have some misunderstanding about the OpenVINS . When I read the code carefully again, I have the following opinions:
literature [1] saves N camera poses in sliding window? So, the Jacobian matrix J of the camera state at time k+1|k relative to the IMU error state is calculated, and the system covariance P matrix is updated with equation (15)(16)in [1] .
But the sliding window of OpenVINS saves N IMU states, which are called anchor pose. So, when a new IMU state is added to the sliding window, the system covariance matrix is directly copy the variance related to the IMU.
Is this statement correct?
[1] Mourikis A I, Roumeliotis S I. A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation[C]//ICRA. 2007, 2: 6.
Yes
It is equivalent to understanding that the J in [1] equals the identity matrix in OpenVINS.
On Sat, Mar 12, 2022 at 6:28 AM Woosik Lee @.***> wrote:
Yes
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Thank you for sharing this multifunctional platform. I encountered the following problems when reading the code. Adding a new camera state to the system state in MSCKF requires augmenting the covariance, such as (15)(16)in [1], but I didn't see the corresponding code segment when I read the code, can you give me some guidance?
[1] Mourikis A I, Roumeliotis S I. A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation[C]//ICRA. 2007, 2: 6.