A Code for Unscented Kalman Filtering on Manifolds (UKF-M)

by   Martin Brossard, et al.

The present paper introduces a novel methodology for Unscented Kalman Filtering (UKF) on manifolds that extends our previous work about UKF on Lie groups. Beyond filtering performances, the main interests of the approach are its versatility, as the method applies to numerous state estimation problems, and its simplicity of implementation for practitioners not being necessarily familiar with manifolds and Lie groups. We have developed the method on two independent open-source Python and Matlab frameworks we call UKF-M, for quickly implementing and testing the approach. The online repositories contain tutorials, documentations, and various relevant robotics examples that the user can readily reproduce and then adapt, for fast prototyping and benchmarking. The code is available at https://github.com/CAOR-MINES-ParisTech/ukfm.


Embedding manifold structures into Kalman filters

Error-state Kalman filter is an elegant and effective filtering techniqu...

Tangent Space Backpropagation for 3D Transformation Groups

We address the problem of performing backpropagation for computation gra...

Watts: Infrastructure for Open-Ended Learning

This paper proposes a framework called Watts for implementing, comparing...

KFNet: Learning Temporal Camera Relocalization using Kalman Filtering

Temporal camera relocalization estimates the pose with respect to each v...

Trajectory Optimization on Matrix Lie Groups with Differential Dynamic Programming and Nonlinear Constraints

Matrix Lie groups are an important class of manifolds commonly used in c...

EvoCraft: A New Challenge for Open-Endedness

This paper introduces EvoCraft, a framework for Minecraft designed to st...

SymForce: Symbolic Computation and Code Generation for Robotics

We present SymForce, a library for fast symbolic computation, code gener...

Please sign up or login with your details

Forgot password? Click here to reset