53 citations · 122 across the 15 of their papers we have counts for
17 papers
Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining
Christopher Leet, Paolo Forte, Uwe Köckemann +2
Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coordinating fleets of autonomo…
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…
How-to Augmented Lagrangian on Factor Graphs
Barbara Bazzana, Henrik Andreasson, Giorgio Grisetti
Factor graphs are a very powerful graphical representation, used to model many problems in robotics. They are widely spread in the areas of Simultaneous Localization and Mapping (S…
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…
Software Architectures for Mobile Robots
Henrik Andreasson, Giorgio Grisetti, Todor Stoyanov +1
A software architecture defines the blueprints of a large computational system, and is thus a crucial part of the design and development effort. This task has been explored extensi…
Sensors for Mobile Robots
Henrik Andreasson, Giorgio Grisetti, Todor Stoyanov +1
A sensor is a device that converts a physical parameter or an environmental characteristic (e.g., temperature, distance, speed, etc.) into a signal that can be digitally measured a…
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…
Autonomous Heavy-Duty Mobile Machinery: A Multidisciplinary Collaborative Challenge
Tyrone Machado, David Fassbender, Abdolreza Taheri +10
Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortag…
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…
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…
SLAM auto-complete: completing a robot map using an emergency map
Malcolm Mielle, Martin Magnusson, Henrik Andreasson +1
In search and rescue missions, time is an important factor; fast navigation and quickly acquiring situation awareness might be matters of life and death. Hence, the use of robots i…