Comments (1)
That’s the claim, but in practice it will probably depend on the structure and dimension of your state and measurement vectors, and also the relative efficiency of the various linear algebra routines in Eigen (QR decomposition, LLT decomposition etc).
For instance, in the benchmarks, the non-square root implementation actually seems to be a little faster in the artificial example I’ve provided, although I haven’t looked in detail into why that is. It’s likely that my implementation of the square-root UKF could be further optimised.
In my experience, the real advantage of the square-root UKF is its considerably improved numerical stability.
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Related Issues (20)
- error in initialization of quaternions HOT 4
- ukf library in ROS project HOT 2
- Request for CKF. HOT 1
- Does MeasurementVector support quaternion? HOT 13
- CMake compatibility HOT 2
- compile error
- Bazel build, for hermetic reproducible builds
- Why are the measurement vector variances declared static? HOT 2
- Question: How to predict next state with UKF? HOT 1
- Question: Multiple measurements from the same sensor between updates HOT 1
- Eigen Patch HOT 2
- Can't link to separate executable: Integrator can't find custom derivative functions HOT 3
- template error with clang HOT 5
- what is the significance of the `0.01` in `test_filter.step(0.01, meas)` ?
- Is there some principled way of utilising confidence of detection?
- How to handle the second-ordered term in calculating the derivative term for state vector ?
- How should the measurement field names be in comparison with state field names?
- Does UKF have smoothing?
- Rotation estimation
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