53 citations · 86 across the 12 of their papers we have counts for
14 papers
Graph Theoretical Outlier Rejection for 4D Radar Registration in Feature-Poor Environments
Georg Dorndorf, Daniel Adolfsson, Masrur Doostdar
Automotive 4D imaging radar is well suited for operation in dusty and low-visibility environments, but scan registration remains challenging due to scan sparsity and spurious detec…
CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization
Maximilian Hilger, Daniel Adolfsson, Ralf Becker +2
Reliable localization in prior maps is essential for autonomous navigation, particularly under adverse weather, where optical sensors may fail. We present CFEAR-TR, a teach-and-rep…
Introspective Loop Closure for SLAM with 4D Imaging Radar
Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson +3
Simultaneous Localization and Mapping (SLAM) allows mobile robots to navigate without external positioning systems or pre-existing maps. Radar is emerging as a valuable sensing too…
Towards introspective loop closure in 4D radar SLAM
Maximilian Hilger, Vladimír Kubelka, Daniel Adolfsson +2
Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates th…
An evaluation of CFEAR Radar Odometry
Daniel Adolfsson, Maximilian Hilger
This article describes the method CFEAR Radar odometry, submitted to a competition at the Radar in Robotics workshop, ICRA 20241. CFEAR is an efficient and accurate method for spin…
TBV Radar SLAM -- trust but verify loop candidates
Daniel Adolfsson, Mattias Karlsson, Vladimír Kubelka +2
Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present T…
Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
Daniel Adolfsson, Martin Magnusson, Anas Alhashimi +2
This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across ve…
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…
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
Li Sun, Daniel Adolfsson, Martin Magnusson +3
This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based loca…