Comments (6)
Well it is a bit like the bicycle, you need to avance to better feel.
from ai-imu-dr.
Hi,
Your question is very interesting. As you said, a major question we have to answer is:
"Can we estimate forward accelerometer bias ?"
To answer this question, we can perform an observability analysis (see e.g. this paper). With this analysis, we can see that forward accelerometer bias is observable if the car rotates. My analysis is still a paper draft, I will share you once it is well written.
To understand this good news, we can move from theory to intuition. Assume at time n, you integrate forward bias and the yaw is increasing. At time n+1, a part of the bias is present in the lateral velocity, such that zero lateral velocity measurement gives information about the forward bias value.
from ai-imu-dr.
Hi Brossard,
I really appreciated your quick answer. I still have some other questions.
Do you think a litter turn left and then back can correct the estimated bias error? for example changing the lane of your car. Since in your experiments, there are very long straight road.
Do you think your algorithm can deal with wheeled robot? In my mind, there are at least 2 cases which may not happen for cars: serious slippage (can go forward but not along a straight line) and nearly pure rotation.
Thanks a lot.
Yanming
from ai-imu-dr.
Hello,
Do you think a litter turn left and then back can correct the estimated bias error? for example
changing the lane of your car. Since in your experiments, there are very long straight road.
It depends on the bias values. If biases are small as in the KITTI dataset, it would work. However, I test the method on another data with important biases (order 10^-3 m/s^2). The position estimates drift in several highway sequences until a 90° curve is performed and then estimation is corrected and accurate for the rest of the sequences. One way to improve the bias estimation is to start by stopping and perform "zero acceleration measurement".
Do you think your algorithm can deal with wheeled robot? In my mind, there are at least 2 cases which may not happen for cars: serious slippage (can go forward but not along a straight line) and nearly pure rotation.
I assume it will work well for nearly pure rotation since null lateral and vertical velocity assumptions are valid and in this case provide orientation information.
For serious slippage, the method would presumably badly work. This can be attenuated is the robot is indoor and that its vertical velocity is almost always null.
from ai-imu-dr.
Hi Brossard,
I am now interested at the observability analysis of inertial navigation with nonholonomic constraints. I think your new paper is on this topic. So I am really expecting your new paper. Is it ready for publish?
Actually I mainly focus on ground robot with wheel. The 2 main motion patterns of ground robots are: turning with constant speed and moving along a straight line. According to the paper "VINS on Wheels", both of these 2 patterns can add additional unobservable directions to VINS.
For system with IMU and nonholonomic constraints only (INS), I think turning has no problem. Am I right? In the case of moving along a straight line without rotation, are peach and roll unobservable?
Besides unobservability, the speed of drift (error accumulation) is also an interesting problem. Could we compare the drift speed of VINS and INS with constraints? Do you know if there has been some research in this field?
Best regards
Yanming
from ai-imu-dr.
Hello,
The paper is currently still in review.
I also think understanding observability/unobservability of a system could help the estimation. As you said, for a wheeled robot, turning is not a problem.
However, in straight line, peach and roll as they can confused with bias.
In my knowledge, there are not so much research in that field. You can look at the other papers of the authors of Vins on wheels, as the paper of the team of Guoquan (Paul) Huang, e.g. Degenerate Motion Analysis for Aided INS With Online Spatial and Temporal Sensor Calibration.
from ai-imu-dr.
Related Issues (20)
- The data file is not found HOT 13
- For different types of robots, where in the code does the algorithm need to change the vehicle model, or does it need to be changed?
- Training on a GPU HOT 1
- Question about the real time ability about the proposed approach HOT 1
- Issue on the ai-imu-dr with the compiling and running python main file. HOT 7
- Cannot access url for data and train parameters HOT 1
- train parameters size mismatch HOT 37
- Case 2011_10_03_drive_0027_extract - what happened?
- fixing the torch.gesv in utils_torch_filter.py HOT 3
- Train filter failure with error: TypeError: zeros() received an invalid combination of arguments - got (NoneType, int, int), HOT 6
- about sup.pdf HOT 5
- Code's equation not same with the paper HOT 21
- The checkpoint is not same with current model? Fail to load state_dict
- Reason for no end frame for some of the sequences
- Eq. 6 of Brossard's paper: Rotation matrix must be confined in x-y plane. HOT 6
- Issues with the comparison between the estimated position and ground-truth one
- Anyone knows what's the meaning of odometry_benchmark and odometry_benchmark_img? HOT 1
- Eq. 11 of the propagation step different from code (?) HOT 4
- unmatched iekfnets.p from dropbox HOT 5
- Asking for KITTI origin imu data(containing the origin timestamp file) of 09_30_0028
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from ai-imu-dr.