28 citations · 64 across the 11 of their papers we have counts for
11 papers
Reformulating AI-based Multi-Object Relative State Estimation for Aleatoric Uncertainty-based Outlier Rejection of Partial Measurements
Thomas Jantos, Giulio Delama, Stephan Weiss +1
Precise localization with respect to a set of objects of interest enables mobile robots to perform various tasks. With the rise of edge devices capable of deploying deep neural net…
Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects.…
Learning Point Correspondences In Radar 3D Point Clouds For Radar-Inertial Odometry
Jan Michalczyk, Stephan Weiss, Jan Steinbrener
Using 3D point clouds in odometry estimation in robotics often requires finding a set of correspondences between points in subsequent scans. While there are established methods for…
Sensor Model Identification via Simultaneous Model Selection and State Variable Determination
Christian Brommer, Alessandro Fornasier, Jan Steinbrener +1
We present a method for the unattended gray-box identification of sensor models commonly used by localization algorithms in the field of robotics. The objective is to determine the…
CaRoSaC: A Reinforcement Learning-Based Kinematic Control of Cable-Driven Parallel Robots by Addressing Cable Sag through Simulation
Rohit Dhakate, Thomas Jantos, Eren Allak +2
This paper introduces the Cable Robot Simulation and Control (CaRoSaC) Framework, which integrates a simulation environment with a model-free reinforcement learning control methodo…
Autonomous Control of Redundant Hydraulic Manipulator Using Reinforcement Learning with Action Feedback
Rohit Dhakate, Christian Brommer, Christoph Böhm +2
This article presents an entirely data-driven approach for autonomous control of redundant manipulators with hydraulic actuation. The approach only requires minimal system informat…
AIVIO: Closed-loop, Object-relative Navigation of UAVs with AI-aided Visual Inertial Odometry
Thomas Jantos, Martin Scheiber, Christian Brommer +3
Object-relative mobile robot navigation is essential for a variety of tasks, e.g. autonomous critical infrastructure inspection, but requires the capability to extract semantic inf…
AI-Based Multi-Object Relative State Estimation with Self-Calibration Capabilities
Thomas Jantos, Christian Brommer, Eren Allak +2
The capability to extract task specific, semantic information from raw sensory data is a crucial requirement for many applications of mobile robotics. Autonomous inspection of crit…
PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation
Thomas Jantos, Mohamed Amin Hamdad, Wolfgang Granig +2
Accurate 6D object pose estimation is an important task for a variety of robotic applications such as grasping or localization. It is a challenging task due to object symmetries, c…
INSANE: Cross-Domain UAV Data Sets with Increased Number of Sensors for developing Advanced and Novel Estimators
Christian Brommer, Alessandro Fornasier, Martin Scheiber +4
For real-world applications, autonomous mobile robotic platforms must be capable of navigating safely in a multitude of different and dynamic environments with accurate and robust…
Variational Inference to Measure Model Uncertainty in Deep Neural Networks
Konstantin Posch, Jan Steinbrener, Jürgen Pilz
We present a novel approach for training deep neural networks in a Bayesian way. Classical, i.e. non-Bayesian, deep learning has two major drawbacks both originating from the fact…