activity
20172022
most citedCorAl: Introspection for Robust Radar and Lidar Perception in Diverse Environments Using Differential Entropy

19 citations · 52 across the 9 of their papers we have counts for

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11 papers · 1 filter

cs.RO202219 cited

CorAl: Introspection for Robust Radar and Lidar Perception in Diverse Environments Using Differential Entropy

Daniel Adolfsson, Manuel Castellano-Quero, Martin Magnusson +2

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and preprocessing, as well…

cs.RO2021

Oriented surface points for efficient and accurate radar odometry

Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2

This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth…

cs.RO2021

CorAl -- Are the point clouds Correctly Aligned?

Daniel Adolfsson, Martin Magnusson, Qianfang Liao +2

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and wi…

cs.RO20215 cited

BFAR-Bounded False Alarm Rate detector for improved radar odometry estimation

Anas Alhashimi, Daniel Adolfsson, Martin Magnusson +2

This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Conti…

cs.RO2021

CFEAR Radarodometry -- Conservative Filtering for Efficient and Accurate Radar Odometry

Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2

This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keep…

cs.RO2021

Learning Occupancy Priors of Human Motion from Semantic Maps of Urban Environments

Andrey Rudenko, Luigi Palmieri, Johannes Doellinger +2

Understanding and anticipating human activity is an important capability for intelligent systems in mobile robotics, autonomous driving, and video surveillance. While learning from…