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SCALE AMBIGUITY #3
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Hello @logindiamant,
Thank you for your question. I am taking differential observations (i.e. velocities) from the monocular DSO and absolute observations from GPS (i.e. positions in cartesian coordinates). I do not model the scale drift in the Kalman Filter formulation, therefore the method has clear drawbacks which can be improved. They are discussed in the published Paper which you can download here (this is the preprint version): http:https://master.kalisz.co/Kalisz_BSLAMSIM_ShortVersion.pdf I hope this helps you and would love to hear your constructive feedback. Kind regards, |
Thanks for you answer, Adam! Kind regards, |
Hi Egor, Thank you for your explanation. I think using ICP is a good idea. However, it willl most likely only work if the Visual SLAM algorithm does not suffer from Scale Drift, i.e. the scale ambiguity remains constant across the whole trajectory. Otherweise you would need some ARAP (As Rigid As Possible) approaches such as global bundle adjustment algorithms. We have investigated incoorporating the GPS measurements directly into the objective function of Visual SLAM, but are not happy with the derivation of the formulas at the moment. The bottom line is, I think that both of our approaches are very important for a robust sensor data fusion and we should definitely continue to investigate them. I am very happy that you are on the topic as well and thank you for sharing your work. I wish you good success with your research and will constantly share my progress in the future! Kind regards, |
Dear, Adam!
Please tell, how you solve scale ambiguity problem and fuse GPS and SLAM data with different scale in Kalman filter?
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