arXiv subjects

Henrik Andreasson

Publications and source records attributed to Henrik Andreasson.

At least 19 recordsLinked to original sources

arXiv2026

Eventually Optimal and Scalable Multi-Agent Planning for Block Cave Mining

Automation in underground mining has the potential to significantly enhance safety, operational efficiency, and sustainability. However, effectively coordinating fleets of autonomous vehicles in dynamic mine environments introduces substantial challenges in both optimization and motion planning. To address these challenges, we introduce and formalize the \emph{Block Cave Mining (BCM)} problem, which focuses on computing a transport plan that maximizes ore throughput while satisfying draw ratio constraints. To solve this problem, we propose SAMM, an eventually optimal anytime solver that jointly integrates task assignment, scheduling, and path planning via a mixed-integer linear programming formulation. To improve scalability, we also introduce SAMMS, a variant of SAMM that trades optimality guarantees for efficiency by decomposing the problem into shorter planning subcycles. Experimental evaluations using realistic industrial mine scenarios demonstrate that SAMMS achieves near-optimal throughput and scales effectively to larger fleets and mine layouts.

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arXiv2026

Neural Surface and Reflectance Modelling from 3D Radar Data

Robust scene representation is essential for autonomous systems to safely operate in challenging low-visibility environments. In these conditions, radar has a clear advantage over cameras and lidars due to its resilience to environmental factors such as fog, smoke, or dust. However, radar data is inherently sparse and noisy, making reliable 3D surface reconstruction challenging. To address this, we propose a neural implicit approach for 3D mapping from radar point clouds that jointly models scene geometry and view-dependent radar intensities. Our method leverages a memory-efficient hybrid feature encoding to learn a continuous Signed Distance Field (SDF) for surface reconstruction, while also capturing radar-specific reflective properties. We show that our approach produces smoother, more accurate 3D surface reconstructions compared to existing lidar-based reconstruction methods applied to radar data and can reconstruct view-dependent radar intensities. We also show that, in general, as input point clouds get sparser, neural implicit representations render more faithful surfaces than traditional explicit SDFs and meshing techniques.

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arXiv2026

CFEAR-Teach-and-Repeat: Fast and Accurate Radar-only Localization

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-repeat localization pipeline using a single spinning radar, which is designed for easily deployable, lightweight, and robust navigation in adverse conditions. Our method localizes by jointly aligning live scans to both stored scans from the teach mapping pass, and to a sliding window of recent live keyframes. This ensures accurate and robust pose estimation across different seasons and weather phenomena. Radar scans are represented using a sparse set of oriented surface points, computed from Doppler-compensated measurements. The map is stored in a pose graph that is traversed during localization. Experiments on the held-out test sequences from the Boreas dataset show that CFEAR-TR can localize with an accuracy as low as 0.117 m and 0.096°, corresponding to improvements of up to 63% over the previous state of the art, while running efficiently at 29 Hz. These results substantially narrow the gap to lidar-level localization, particularly in heading estimation. We make the C++ implementation of our work available to the community.

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arXiv2025

Single-View Shape Completion for Robotic Grasping in Clutter

In vision-based robot manipulation, a single camera view can only capture one side of objects of interest, with additional occlusions in cluttered scenes further restricting visibility. As a result, the observed geometry is incomplete, and grasp estimation algorithms perform suboptimally. To address this limitation, we leverage diffusion models to perform category-level 3D shape completion from partial depth observations obtained from a single view, reconstructing complete object geometries to provide richer context for grasp planning. Our method focuses on common household items with diverse geometries, generating full 3D shapes that serve as input to downstream grasp inference networks. Unlike prior work, which primarily considers isolated objects or minimal clutter, we evaluate shape completion and grasping in realistic clutter scenarios with household objects. In preliminary evaluations on a cluttered scene, our approach consistently results in better grasp success rates than a naive baseline without shape completion by 23% and over a recent state of the art shape completion approach by 19%. Our code is available at https://amm.aass.oru.se/shape-completion-grasping/.

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arXiv2025

Exploiting Radiance Fields for Grasp Generation on Novel Synthetic Views

Vision based robot manipulation uses cameras to capture one or more images of a scene containing the objects to be manipulated. Taking multiple images can help if any object is occluded from one viewpoint but more visible from another viewpoint. However, the camera has to be moved to a sequence of suitable positions for capturing multiple images, which requires time and may not always be possible, due to reachability constraints. So while additional images can produce more accurate grasp poses due to the extra information available, the time-cost goes up with the number of additional views sampled. Scene representations like Gaussian Splatting are capable of rendering accurate photorealistic virtual images from user-specified novel viewpoints. In this work, we show initial results which indicate that novel view synthesis can provide additional context in generating grasp poses. Our experiments on the Graspnet-1billion dataset show that novel views contributed force-closure grasps in addition to the force-closure grasps obtained from sparsely sampled real views while also improving grasp coverage. In the future we hope this work can be extended to improve grasp extraction from radiance fields constructed with a single input image, using for example diffusion models or generalizable radiance fields.

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arXiv2025

Introspective Loop Closure for SLAM with 4D Imaging Radar

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 tool, especially in vision-obstructed environments, as it is less affected by particles than lidars or cameras. Modern 4D imaging radars provide three-dimensional geometric information and relative velocity measurements, but they bring challenges, such as a small field of view and sparse, noisy point clouds. Detecting loop closures in SLAM is critical for reducing trajectory drift and maintaining map accuracy. However, the directional nature of 4D radar data makes identifying loop closures, especially from reverse viewpoints, difficult due to limited scan overlap. This article explores using 4D radar for loop closure in SLAM, focusing on similar and opposing viewpoints. We generate submaps for a denser environment representation and use introspective measures to reject false detections in feature-degenerate environments. Our experiments show accurate loop closure detection in geometrically diverse settings for both similar and opposing viewpoints, improving trajectory estimation with up to 82 % improvement in ATE and rejecting false positives in self-similar environments.

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arXiv2024

Learning the Approach During the Short-loading Cycle Using Reinforcement Learning

The short-loading cycle is a repetitive task performed in high quantities, making it a great alternative for automation. In the short-loading cycle, an expert operator navigates towards a pile, fills the bucket with material, navigates to a dump truck, and dumps the material into the tipping body. The operator has to balance the productivity goal while minimising the fuel usage, to maximise the overall efficiency of the cycle. In addition, difficult interactions, such as the tyre-to-surface interaction further complicate the cycle. These types of hard-to-model interactions that can be difficult to address with rule-based systems, together with the efficiency requirements, motivate us to examine the potential of data-driven approaches. In this paper, the possibility of teaching an agent through reinforcement learning to approach a dump truck's tipping body and get in position to dump material in the tipping body is examined. The agent is trained in a 3D simulated environment to perform a simplified navigation task. The trained agent is directly transferred to a real vehicle, to perform the same task, with no additional training. The results indicate that the agent can successfully learn to navigate towards the dump truck with a limited amount of control signals in simulation and when transferred to a real vehicle, exhibits the correct behaviour.

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arXiv2024

Towards introspective loop closure in 4D radar SLAM

Imaging radar is an emerging sensor modality in the context of Localization and Mapping (SLAM), especially suitable for vision-obstructed environments. This article investigates the use of 4D imaging radars for SLAM and analyzes the challenges in robust loop closure. Previous work indicates that 4D radars, together with inertial measurements, offer ample information for accurate odometry estimation. However, the low field of view, limited resolution, and sparse and noisy measurements render loop closure a significantly more challenging problem. Our work builds on the previous work - TBV SLAM - which was proposed for robust loop closure with 360$^\circ$ spinning radars. This article highlights and addresses challenges inherited from a directional 4D radar, such as sparsity, noise, and reduced field of view, and discusses why the common definition of a loop closure is unsuitable. By combining multiple quality measures for accurate loop closure detection adapted to 4D radar data, significant results in trajectory estimation are achieved; the absolute trajectory error is as low as 0.46 m over a distance of 1.8 km, with consistent operation over multiple environments.

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arXiv2023

Sensors for Mobile Robots

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 and processed to perform specific tasks. Mobile robots need sensors to measure properties of their environment, thus allowing for safe navigation, complex perception and corresponding actions, and effective interactions with other agents that populate it. Sensors used by mobile robots range from simple tactile sensors, such as bumpers, to complex vision-based sensors such as structured light RGB-D cameras. All of them provide a digital output (e.g., a string, a set of values, a matrix, etc.) that can be processed by the robot's computer. Such output is typically obtained by discretizing one or more analog electrical signals by using an Analog to Digital Converter (ADC) included in the sensor. In this chapter we present the most common sensors used in mobile robotics, providing an introduction to their taxonomy, basic features, and specifications. The description of the functionalities and the types of applications follows a bottom-up approach: the basic principles and components on which the sensors are based are presented before describing real-world sensors, which are generally based on multiple technologies and basic devices.

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arXiv2023

Software Architectures for Mobile Robots

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 extensively in the context of mobile robots, resulting in a plethora of reference designs and implementations. As the software architecture defines the framework in which all components are implemented, it is naturally a very important aspect of a mobile robot system. In this chapter, we overview the requirements that the particular problem domain (a mobile robot system) imposes on the software framework. We discuss some of the current design solutions, provide a historical perspective on common frameworks, and outline directions for future development.

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arXiv2023

How-to Augmented Lagrangian on Factor Graphs

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 (SLAM), computer vision, and localization. In this paper we describe an approach to fill the gap with other areas, such as optimal control, by presenting an extension of Factor Graph Solvers to constrained optimization. The core idea of our method is to encapsulate the Augmented Lagrangian (AL) method in factors of the graph that can be integrated straightforwardly in existing factor graph solvers. We show the generality of our approach by addressing three applications, arising from different areas: pose estimation, rotation synchronization and Model Predictive Control (MPC) of a pseudo-omnidirectional platform. We implemented our approach using C++ and ROS. Besides the generality of the approach, application results show that we can favorably compare against domain specific approaches.

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arXiv2023

TBV Radar SLAM -- trust but verify loop candidates

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 TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a fault-resilient odometry pipeline within a pose graph framework. By evaluating on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it's generalizing across environments without needing to change any parameters.

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arXiv2023

Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments

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 very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.

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arXiv2022

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

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 as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-stage filtering technique that produces high-quality point clouds from noisy radar scans. Thus we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

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arXiv2022

Autonomous Heavy-Duty Mobile Machinery: A Multidisciplinary Collaborative Challenge

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 shortage, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human-operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce a conceptual understanding of collaborations in the autonomous HDMM as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks.

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arXiv2021

Oriented surface points for efficient and accurate radar odometry

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 that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training.

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arXiv2021

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

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 Continuous Wave (FMCW) radars can be useful for localization and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR to the use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5deg/100 m to 1.55%/0.46deg/100 m; an improvement of 12.5%.

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arXiv2021

CorAl -- Are the point clouds Correctly Aligned?

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 without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

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