I'm trying to finish up a localization pipeline, and the last module I need is a filtering framework for my pose estimates. While a Kalman filter is probably the most popular option, I'm using cameras for my sensing and I wouldn't be able to ensure the kinds of noise profiles KF is good for, I doubt it would work as well with suddenly appearing outliers in my poses: so I am looking for other options which can work with real time data and be immune to outliers.
One thing I came across is a Kalman filter with a threshold based update rejection, something like Mahalanobis distance: but I don't know if this is completely applicable because the localization would be performed in real time, and it's not like I have a large set of 'good poses' to start with. The end result I'm expecting is something like smoothing, but without access to a full set of values. Another option I found is from a paper that proposes a 'robust Bayesian weighted Kalman filter' that's good for real time outlier rejection, but I still need to read through it: and I don't have much experience with filtering/smoothing, so any suggestions would be very helpful, perhaps about a decent go-to mechanism for this?