Globally rising demand for transportation by rail is pushing existing
in...
Reliable obstacle detection on railways could help prevent collisions th...
Volumetric maps are widely used in robotics due to their desirable prope...
Natural environments such as forests and grasslands are challenging for
...
Mobile ground robots require perceiving and understanding their surround...
Integration of multiple sensor modalities and deep learning into Simulta...
An increasing amount of applications rely on data-driven models that are...
The exploration of large-scale unknown environments can benefit from the...
Semantic segmentation for robotic systems can enable a wide range of
app...
Numerous applications require robots to operate in environments shared w...
Exploration of unknown environments is a fundamental problem in robotics...
In order to operate in human environments, a robot's semantic perception...
This work investigates the use of Neural implicit representations,
speci...
Current global localization descriptors in Simultaneous Localization and...
This work presents an embodied agent that can adapt its semantic segment...
In this paper, we deal with the problem of creating globally consistent ...
Semantic segmentation networks are usually pre-trained and not updated d...
We present a novel 3D mapping method leveraging the recent progress in n...
Autonomous robots deal with unexpected scenarios in real environments. G...
For robotic interaction in an environment shared with multiple agents,
a...
Introducing semantically meaningful objects to visual Simultaneous
Local...
We propose a novel robotic system that can improve its semantic percepti...
In this paper, we propose a robust end-to-end multi-modal pipeline for p...
Many mobile robotic platforms rely on an accurate knowledge of the extri...
The inability of state-of-the-art semantic segmentation methods to detec...
This paper presents a novel on-line path planning method that enables ae...
We propose PHASER, a correspondence-free global registration of
sensor-c...
In this paper, we present a semantic mapping approach with multiple
hypo...
The distribution of a neural network's latent representations has been
s...
Visual-inertial systems rely on precise calibrations of both camera
intr...
Neural networks (NNs) are widely used for object recognition tasks in
au...
Exploration is a fundamental problem in robot autonomy. A major limitati...
Localization of a robotic system within a previously mapped environment ...
In this paper we present a data-driven approach to obtain the static ima...
In this paper, we present our deep learning-based human detection system...
This paper presents a localization system for mobile robots enabling pre...
Self-diagnosis and self-repair are some of the key challenges in deployi...
Globally consistent dense maps are a key requirement for long-term robot...
Visually poor scenarios are one of the main sources of failure in visual...
This paper tackles the problem of data fusion in the semantic scene
comp...
Semantic Scene Completion (SSC) refers to the task of inferring the 3D
s...
Robust and accurate pose estimation is crucial for many applications in
...
Safe and efficient path planning is crucial for autonomous mobile robots...
Precisely estimating a robot's pose in a prior, global map is a fundamen...
There has been a remarkable progress in the accuracy of semantic segment...
In this paper, we propose a visual-inertial framework able to efficientl...
In many applications, maintaining a consistent map of the environment is...
Deep learning has enabled remarkable advances in semantic segmentation a...
Teams of UGVs patrolling harsh and complex 3D environments can experienc...
Deep learning has enabled impressive progress in the accuracy of semanti...