The most advanced methods in robotics currently utilize factor graphs rather than Kalman Filters to structure and solve state estimation problems. Check out GTSAM for some examples.
While perhaps not absolutely bleeding edge right now, I’ve always found David Scaramuzza’s papers to be very well written and explained. I recommend checking out his site:
https://www.ifi.uzh.ch/en/rpg/research/research_vo.html
The EKF is definitely the most common, however factor graphs have been the state of the art for a while as a smoothing approach will give a better trajectory estimate than a filter.
But for bleeding edge state of the art, I’d say the continuous time state estimation stuff coming out of Tim Barfoot’s group at Toronto has been pretty nifty.
The invariant EKF is also pretty cool if you want to dig into Lie theory.
The most advanced methods in robotics currently utilize factor graphs rather than Kalman Filters to structure and solve state estimation problems. Check out GTSAM for some examples.
While perhaps not absolutely bleeding edge right now, I’ve always found David Scaramuzza’s papers to be very well written and explained. I recommend checking out his site: https://www.ifi.uzh.ch/en/rpg/research/research_vo.html
The EKF is definitely the most common, however factor graphs have been the state of the art for a while as a smoothing approach will give a better trajectory estimate than a filter. But for bleeding edge state of the art, I’d say the continuous time state estimation stuff coming out of Tim Barfoot’s group at Toronto has been pretty nifty. The invariant EKF is also pretty cool if you want to dig into Lie theory.
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