Bayes+Estimate Activity
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Activity for Bayes+Estimate
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Michael Stevens committed [5d6c64] on FastSLAM
nalgebra 0.34
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nalgebra 0.34
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Document new_weights
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Hello Adrain, I seen this too. The are lots of ways the sigma transformation can be parametrised. In my UKF implementation I try follow the numerics in various publications and implementations with alpha controlling the spread. The numerics don't seem to be great and I don't see a solution at the moment. If you can build Jacobians out of your hopefully not too non-linear model the the UD filter should be numerically stable. Nalgebra 0.34 is based on Rust edition 2024 which fit nicely with my recent...
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Adrian Figueroa modified a comment on ticket #8
Hello again! I did some more testing and I may have run into numerical issues. When choosing small values for alpha, the sigma point spread is very small. I ran the UKF on f32 number format, so that can cause issues. When alpha is bigger, it appears to be stable. On a new project with a totally different process and implementation, both 0.17.0 and 0.18.1 behave similarly (except if I choose alpha to be too small). Maybe one more thing: there is an updated version of nalgebra, in case you want to...
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Hello again! I did some more testing and I may have run into numerical issues. When choosing small values for alpha, the sigma point spread is very small. I ran the UKF on f32 number format, so that can cause issues. When alpha is bigger, it appears to be stable. On a new project with a different totally implementation, both 0.17.0 and 0.18.1 behave similarly (except if I choose alpha to be too small). Maybe one more thing: there is an updated version of nalgebra, in case you want to move to it....
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Test unscented with alpha = 1e-3
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Slightly improve new_weight to avoid a division
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Interestingly the earlier Wan & Merwe paper 'The Unscented KalmanFilter for Nonlinear Estimation' has a 'kappa' in the formulation of weights. It has a completely different definition to that in the original Julier & Uhlman paper. They simply state 'kappa is a secondary scaling parameter which is usually set to 0'!
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I've retested. 'simple_unscented' with alpha 1e-3 give identical results as alpha 1, which is to be expected as the 'simple' test is linear. 'rtheta_unscented' with alpha 1e-3 changes a little but looks reasonable. Since you are only having a problem in observe I wonder if the problem is related to the size of the observation vector. The test I have it only a two dimensional observation. What are the dimensions of your models. Is the observation function discontinues?
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Yes, I can use a fork that I modified already, no problem. Can I somehow help with testing the unscented approach? By the way, the output of the predict step didn't change much with the new implementation, only observe.
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Yes, I can use a fork that I modified already, no problem. Can I somehow help with testing the unscented approach? By the way, the predict step didn't change much with the new implementation, only observe.
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Annoying. The new implementation should follow that in the Merwe and Wan paper. I test with alpha =1 which is at the opposite extreme from alpha = 0.1. In the paper there mention alpha = 0.001 Looks like I need to do more testing. The only other implementation I found is in Python again changes the parametersation and reintroduces a kappa. I can find no publication mention or any mathematical justification so I have avoided that. Maybe just copy the old unscented_duplex from older releases back into...
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Thanks! We now get vastly different/incorrect output in the observe step of the unscented estimator using alpha = 0.1 and beta = 2.0 as opposed to the old implementation. Is there something else to be aware of?
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Thanks! We now get vastly different/incorrect output in the observe step of the unscented estimator using alpha = 0.01 and beta = 2.0 as opposed to the old implementation. Is there something else to be aware of?
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Opps, the module docstring was not updated. Sigma point parametrisation are identical to the SRUF in Van der Merwe paper. Effectively kappa is replaced by alpha and beta. I have pushed a corrected docstring in "main", thanks. There are various preprint and papers from Simon Julier titled "The scaled unscented transform" that introduce and analyse the alpha and beta parametrisation for choosing sigma points.
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Correct unscented docstring regarding sigma points
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Great, thanks for your efforts! I wonder, what method is now used for sigma point generation? Is it still the same? The module docstring appears to be out of data, since it still mentions kappa, although it doesn't exist as a parameter anymore. Looks like you moved to Van der Merwe's method already, correct? So kappa is always zero if I read your equations correctly.
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Great, thanks for your efforts! I wonder, what method is now used for sigma point generation? Is it still the same? The module docstring appears to be out of data, since it still mentions kappa, although it doesn't exist as a parameter anymore. Looks like you moved to Van der Merwe's method already, correct?
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Great, thanks for your efforts! I wonder, what method is now used for sigma point generation? Is it still the same? The module docstring appears to be out of data, since it still mentions kappa, although it doesn't exist as a parameter anymore.
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Bump bayes_estimate 0.18.1 using no_std
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not having std requires features = ["macros", "alloc", "libm"]
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Bump bayes_estimate 0.18.1 using no_std
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Bayes+Estimate released /Bayes++/2025.4-1/bf-C++source-2025.4-1.zip
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Compile and test with gcc-13
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2025.4 results
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Release 2025-4 with CI. Renamed to 'invertable'
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I've pushed version 0.18.1. Since I was only using Vec from std I have changed the library to be completely 'no_std'. There is no 'std' feature anymore making things much simpler. I have removed the old 'unscented_duplex' and replaced it with 'unscented'. This has a more modern parameterisation of the 'sigma' point weights. I've implemented 'unscented_root'. I have marked it as experimental.
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Bump 0.8.0
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0.18.0 requires nalgebra/macros
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0.18.0 requires nalgebra/macros
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Bump 0.18.0
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Bump 0.7.0
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Document test with D2, DYN2, DYN3
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cleanup and document test_estimators
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unscented and unscented_root tested
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UnscentedWeights for mean, and covariance
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implement unscented with new parameterisation
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unscented_root cleanup, doc
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Merge branch 'refs/heads/main' into feature/srukf
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unscented_root testing
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unscented_root more working
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edition 2024
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Fix sir_example
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Bug #8 build without std
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unscented_root somewhat working
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unscented_root not quite generic
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Hi Adrain, The implementation you used follows that in the paper https://groups.seas.harvard.edu/courses/cs281/papers/unscented.pdf ? It would be good if we could get that or similar into the library. Michael
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Hi Adrian, I had a look last week into using a nalgebra::Matrix for the Sigma point. I now know why I originally chose to use a std::Vec of nalgebra::Matrix to do this! I think in principle it is possible using the DimSum (with DimAdd) as type constraint. But the whole type signature becomes totally unwieldy. I will scrub that idea and use alloc::Vec so it is buildable with 'no_std'.
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I see. We had previously used Van der Merwe's method. I will try to contribute such a sigma point generation, as it seems like it would be easily doable. I think unscented_duplex should not really use Vec Yes, but I don't think there is a better choice at the moment. As far as I know, rust doesn't yet support calculations with const generics, such as [2 * N + 1] for array sizes, where N is a const generic usize. If a user knew how to calculate the required generic size, then that length could be...
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Thanks for the feedback. I think unscented_duplex should not really use Vec. Better would be to use a nalgebra Matrix which doesn't need alloc if a fixed size is used.
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Please note my unscented_duplex is based on the original paper by Julier and Uhlmann. There are more recent variant which chose the sigma points differently which may be better.
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I think unscented_duplex should not really use Vec. Better would be to use a nalgebra Matrix which doesn't need alloc if a fixed size is used.
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By extension, I wanted to use the unscented_duplex module, so I made it rely on alloc instead of std. Here is the full diff. Sorry for the formatting noise, this is from cargo fmt. diff --git a/Cargo.toml b/Cargo.toml index aa7903a..3ddff25 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -6,18 +6,19 @@ edition = "2024" description = "Bayesian estimation library. Kalman filter, Informatiom, Square root, Information root, Unscented and UD filters. Numerically and dimensionally generic implementation using...
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Unfortunately not. I think there is no official way to solve this problem: https://github.com/rust-lang/cargo/issues/2911
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I've tried this out. Problem is that the tests require std from nalgebra To make that work I need: [dev-dependencies] nalgebra = { version = "0.33", default-features = true } Seems rather clumsy! As the version has to be specified again. Any ideas how to do this better?
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This builds fine in a no_std environment. [package] name = "bayes_estimate" version = "0.17.0" authors = ["Michael Stevens <mail@michael-stevens.de>"] edition = "2024" description = "Bayesian estimation library. Kalman filter, Informatiom, Square root, Information root, Unscented and UD filters. Numerically and dimensionally generic implementation using nalgebra. Provides fast numerically stable estimation solutions." repository = "https://sourceforge.net/p/bayesclasses/rustcode" readme = "README.md"...
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This builds fine in a no_std environment. [package] name = "bayes_estimate" version = "0.17.0" authors = ["Michael Stevens <mail@michael-stevens.de>"] edition = "2024" description = "Bayesian estimation library. Kalman filter, Informatiom, Square root, Information root, Unscented and UD filters. Numerically and dimensionally generic implementation using nalgebra. Provides fast numerically stable estimation solutions." repository = "https://sourceforge.net/p/bayesclasses/rustcode" readme = "README.md"...
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Cannot build rust crate for `no_std`
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edition 2024
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edition 2024
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Test with coupled prediction model
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ud common code in calce method
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Remove unnecessary 'return'
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Remove unused trait Diagonal
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Linting
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seperate lifetime of returned Result error 'str'
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Remove unnecessary 'return'
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Cleanup text and spelling errors
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Nalgebra 0.33 with Allocator without type
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Fix DimMin on from_uncorrelated
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Cleanup text and spelling errors
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Nalgebra 0.33 with Allocator without type
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Move rcond checks
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Doc.
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approx:0.5
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bayes_estimate:0.15.0
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bayes_estimate:0.14.0
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Unnecessary cast
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Cleanup comments
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fix release 0.13.0
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release 0.13.0
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avoid rather restrictive &'static str
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rustfmt
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Build in workspace
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fast-slam Version 0.5.0
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Version 0.12.0
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Version 0.11.0 nalgebra:0.31
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Unnecessary qualifiers
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nalgebra:0.30
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Use 2021 edition
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nalgebra 0.29
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BUMP nalgebra 0.27
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cleanup use analgebra
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