5 citations · 10 across the 3 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
Zhicheng Zhou, Cheng Zhao, Daniel Adolfsson +4
3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in…