activity
20182022
most citedExpectation-Maximization for Adaptive Mixture Models in Graph Optimization

56 citations · 124 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2022★ 24 cited

A Credible and Robust approach to Ego-Motion Estimation using an Automotive Radar

Karim Haggag, Sven Lange, Tim Pfeifer +1

Consistent motion estimation is fundamental for all mobile autonomous systems. While this sounds like an easy task, often, it is not the case because of changing environmental cond…

cs.RO2021★ 17 cited

Advancing Mixture Models for Least Squares Optimization

Tim Pfeifer, Sven Lange, Peter Protzel

Gaussian mixtures are a powerful and widely used tool to model non-Gaussian estimation problems. They are able to describe measurement errors that follow arbitrary distributions an…

cs.CV2020

Factor Graph based 3D Multi-Object Tracking in Point Clouds

Johannes Pöschmann, Tim Pfeifer, Peter Protzel

Accurate and reliable tracking of multiple moving objects in 3D space is an essential component of urban scene understanding. This is a challenging task because it requires the ass…

cs.RO2020★ 17 cited

It is time for Factor Graph Optimization for GNSS/INS Integration: Comparison between FGO and EKF

Weisong Wen, Tim Pfeifer, Xiwei Bai +1

The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS…

cs.RO2019★ 10 cited

Incrementally Learned Mixture Models for GNSS Localization

Tim Pfeifer, Peter Protzel

GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to m…

cs.RO2018★ 56 cited

Expectation-Maximization for Adaptive Mixture Models in Graph Optimization

Tim Pfeifer, Peter Protzel

Non-Gaussian and multimodal distributions are an important part of many recent robust sensor fusion algorithms. In difference to robust cost functions, they are probabilistically f…