TomLKoller
I am a Ph. D. candidate in computer science. My research is focused on model based sensor fusion.
University Of BremenBremen
Pinned Repositories
ADEKF
Automatic Differentiated Extended Kalman Filter (ADEKF) - This is a generic EKF Implementation that uses automatic differentiation to get rid of the need to define Jacobians.
ADEKF_VIZ
Vizualization tools for the ADEKF
AutomaticObservabilityProofsForINS
BaVI-pose-tracking
Particle Filter and least squares based tracking of climbers with IMUs and route maps.
Boxplus-IMM
This repository contains the boxplus-IMM. A generic interacting multiple model filter which can handle manifold structures (e.g. quaternions) in the state space.
Manifold-RTS-Smoother
A Manifold version of the Popular Rauch Tung Striebel Smoother. Uses the EKF Style Update formulas of the RTS-Smoother.
PDF-to-PowerPoint
Python script to cut a PDF Presentation into Slides and fill a Powerpoint presentation with the images of those slides.
ZaVI_TrackCycling
Contains a batch estimator that estimates the pose of a bike in a track race with an IMU as the only sensor. It uses the 3D map of the track and vehicle constraints. See the publication: State observability through prior Knowledge: Tracking Track Cyclers with Inertial Sensors
TomLKoller's Repositories
TomLKoller/ADEKF
Automatic Differentiated Extended Kalman Filter (ADEKF) - This is a generic EKF Implementation that uses automatic differentiation to get rid of the need to define Jacobians.
TomLKoller/Manifold-RTS-Smoother
A Manifold version of the Popular Rauch Tung Striebel Smoother. Uses the EKF Style Update formulas of the RTS-Smoother.
TomLKoller/Boxplus-IMM
This repository contains the boxplus-IMM. A generic interacting multiple model filter which can handle manifold structures (e.g. quaternions) in the state space.
TomLKoller/ADEKF_VIZ
Vizualization tools for the ADEKF
TomLKoller/PDF-to-PowerPoint
Python script to cut a PDF Presentation into Slides and fill a Powerpoint presentation with the images of those slides.
TomLKoller/ZaVI_TrackCycling
Contains a batch estimator that estimates the pose of a bike in a track race with an IMU as the only sensor. It uses the 3D map of the track and vehicle constraints. See the publication: State observability through prior Knowledge: Tracking Track Cyclers with Inertial Sensors
TomLKoller/AutomaticObservabilityProofsForINS
TomLKoller/BaVI-pose-tracking
Particle Filter and least squares based tracking of climbers with IMUs and route maps.