36 citations · 178 across the 30 of their papers we have counts for
8 papers · 1 filter
5G-DIL: Domain Incremental Learning with Similarity-Aware Sampling for Dynamic 5G Indoor Localization
Nisha Lakshmana Raichur, Lucas Heublein, Christopher Mutschler +1
Indoor positioning based on 5G data has achieved high accuracy through the adoption of recent machine learning (ML) techniques. However, the performance of learning-based methods d…
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Lucas Heublein, Nisha L. Raichur, Tobias Feigl +5
The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs. To achieve accurate pos…
Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning
Nisha L. Raichur, Lucas Heublein, Tobias Feigl +3
The primary objective of methods in continual learning is to learn tasks in a sequential manner over time (sometimes from a stream of data), while mitigating the detrimental phenom…
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments
Felix Ott, Lucas Heublein, David Rügamer +2
The localization of objects is a crucial task in various applications such as robotics, virtual and augmented reality, and the transportation of goods in warehouses. Recent advance…
Representation Learning for Tablet and Paper Domain Adaptation in Favor of Online Handwriting Recognition
Felix Ott, David Rügamer, Lucas Heublein +2
The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. The goal of doma…
Benchmarking Visual-Inertial Deep Multimodal Fusion for Relative Pose Regression and Odometry-aided Absolute Pose Regression
Felix Ott, Nisha Lakshmana Raichur, David Rügamer +4
Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate…
Uncertainty-aware Evaluation of Time-Series Classification for Online Handwriting Recognition with Domain Shift
Andreas Klaß, Sven M. Lorenz, Martin W. Lauer-Schmaltz +4
For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for com…
ViPR: Visual-Odometry-aided Pose Regression for 6DoF Camera Localization
Felix Ott, Tobias Feigl, Christoffer Löffler +1
Visual Odometry (VO) accumulates a positional drift in long-term robot navigation tasks. Although Convolutional Neural Networks (CNNs) improve VO in various aspects, VO still suffe…