10 citations · 10 across the 12 of their papers we have counts for
5 papers · 1 filter
Making the Flow Glow -- Robot Perception under Severe Lighting Conditions using Normalizing Flow Gradients
Simon Kristoffersson Lind, Rudolph Triebel, Volker Krüger
Modern robotic perception is highly dependent on neural networks. It is well known that neural network-based perception can be unreliable in real-world deployment, especially in di…
Single-Shot Metric Depth from Focused Plenoptic Cameras
Blanca Lasheras-Hernandez, Klaus H. Strobl, Sergio Izquierdo +3
Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or st…
Unifying Local and Global Multimodal Features for Place Recognition in Aliased and Low-Texture Environments
Alberto García-Hernández, Riccardo Giubilato, Klaus H. Strobl +2
Perceptual aliasing and weak textures pose significant challenges to the task of place recognition, hindering the performance of Simultaneous Localization and Mapping (SLAM) system…
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection
Maria Lyssenko, Christoph Gladisch, Christian Heinzemann +2
Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes ut…
A Safety-Adapted Loss for Pedestrian Detection in Automated Driving
Maria Lyssenko, Piyush Pimplikar, Maarten Bieshaar +2
In safety-critical domains like automated driving (AD), errors by the object detector may endanger pedestrians and other vulnerable road users (VRU). As common evaluation metrics a…