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Sökning: db:Swepub > Örebro universitet > Lilienthal Achim J.

  • Resultat 1-10 av 215
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1.
  • Adolfsson, Daniel, 1992-, et al. (författare)
  • A Submap per Perspective : Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
  • 2019
  • Ingår i: 2019 European Conference on Mobile Robots (ECMR). - : IEEE. - 9781728136059
  • Konferensbidrag (refereegranskat)abstract
    • This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.
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2.
  • Adolfsson, Daniel, 1992-, et al. (författare)
  • CorAl : Introspection for robust radar and lidar perception in diverse environments using differential entropy
  • 2022
  • Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 155
  • Tidskriftsartikel (refereegranskat)abstract
    • Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, 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-step 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 by 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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3.
  • Almqvist, Håkan, 1984-, et al. (författare)
  • Improving Point-Cloud Accuracy from a Moving Platform in Field Operations
  • 2013
  • Ingår i: 2013 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE conference proceedings. - 9781467356411 - 9781467356435 ; , s. 733-738
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a method for improving the quality of distorted 3D point clouds made from a vehicle equipped with a laser scanner moving over uneven terrain. Existing methods that use 3D point-cloud data (for tasks such as mapping, localisation, and object detection) typically assume that each point cloud is accurate. For autonomous robots moving in rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one point cloud, in which case the data will be distorted. The method proposed in this paper is capable of increasing the accuracy of 3D point clouds, without assuming any specific features of the environment (such as planar walls), without resorting to a "stop-scan-go" approach, and without relying on specialised and expensive hardware. Each new point cloud is matched to the previous using normal-distribution-transform (NDT) registration, after which a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. The proposed method increases the accuracy of both the measured platform trajectory and the point cloud. The method is validated on both real-world and simulated data.
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4.
  • Almqvist, Håkan, 1984-, et al. (författare)
  • Improving Point Cloud Accuracy Obtained from a Moving Platform for Consistent Pile Attack Pose Estimation
  • 2014
  • Ingår i: Journal of Intelligent and Robotic Systems. - Dordrecht : Springer Netherlands. - 0921-0296 .- 1573-0409. ; 75:1, s. 101-128
  • Tidskriftsartikel (refereegranskat)abstract
    • We present a perception system for enabling automated loading with waist-articulated wheel loaders. To enable autonomous loading of piled materials, using either above-ground wheel loaders or underground load-haul-dump vehicles, 3D data of the pile shape is needed. However, using common 3D scanners, the scan data is distorted while the wheel loader is moving towards the pile. Existing methods that make use of 3D scan data (for autonomous loading as well as tasks such as mapping, localisation, and object detection) typically assume that each 3D scan is accurate. For autonomous robots moving over rough terrain, it is often the case that the vehicle moves a substantial amount during the acquisition of one 3D scan, in which case the scan data will be distorted. We present a study of auto-loading methods, and how to locate piles in real-world scenarios with nontrivial ground geometry. We have compared how consistently each method performs for live scans acquired in motion, and also how the methods perform with different view points and scan configurations. The system described in this paper uses a novel method for improving the quality of distorted 3D scans made from a vehicle moving over uneven terrain. The proposed method for improving scan quality is capable of increasing the accuracy of point clouds without assuming any specific features of the environment (such as planar walls), without resorting to a “stop-scan-go” approach, and without relying on specialised and expensive hardware. Each new 3D scan is registered to the preceding using the normal-distributions transform (NDT). After each registration, a mini-loop closure is performed with a local, per-scan, graph-based SLAM method. To verify the impact of the quality improvement, we present data that shows how auto-loading methods benefit from the corrected scans. The presented methods are validated on data from an autonomous wheel loader, as well as with simulated data. The proposed scan-correction method increases the accuracy of both the vehicle trajectory and the point cloud. We also show that it increases the reliability of pile-shape measures used to plan an efficient attack pose when performing autonomous loading.
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5.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • 6D scan registration using depth-interpolated local image features
  • 2010
  • Ingår i: Robotics and Autonomous Systems. - Amsterdam, Netherlands : Elsevier. - 0921-8890 .- 1872-793X. ; 58:2, s. 157-165
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper describes a novel registration approach that is based on a combination of visual and 3D range information.To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laserscanner. The matched depth-interpolated image features allows to apply registration with known correspondences.We compare several ICP variants in this paper and suggest an extension that considers the spatial distance betweenmatching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers allscan poses simultaneously.
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6.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • A Minimalistic Approach to Appearance-Based Visual SLAM
  • 2008
  • Ingår i: IEEE Transactions on Robotics. - New York, NY, USA : IEEE. - 1552-3098. ; 24:5, s. 991-1001
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a vision-based approach to SLAM in indoor / outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omni-directional vision sensor, a novel method is introduced based on the relative similarity of neighbouring images. This new method does not require determining distances to image features using multiple view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle qualitatively different environments (without modification of the parameters), that it can cope with violations of the “flat floor assumption” to some degree, and that it scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g. for solving the multi-robot SLAM problem with unknown initial poses.
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7.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • Autonomous transport vehicles : where we are and what is missing
  • 2015
  • Ingår i: IEEE robotics & automation magazine. - 1070-9932 .- 1558-223X. ; 22:1, s. 64-75
  • Tidskriftsartikel (refereegranskat)abstract
    • In this article, we address the problem of realizing a complete efficient system for automated management of fleets of autonomous ground vehicles in industrial sites. We elicit from current industrial practice and the scientific state of the art the key challenges related to autonomous transport vehicles in industrial environments and relate them to enabling techniques in perception, task allocation, motion planning, coordination, collision prediction, and control. We propose a modular approach based on least commitment, which integrates all modules through a uniform constraint-based paradigm. We describe an instantiation of this system and present a summary of the results, showing evidence of increased flexibility at the control level to adapt to contingencies.
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8.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • Mini-SLAM : minimalistic visual SLAM in large-scale environments based on a new interpretation of image similarity
  • 2007
  • Ingår i: 2007 IEEE international conference on robotics and automation (ICRA). - New York, NY, USA : IEEE. - 9781424406012 ; , s. 4096-4101
  • Konferensbidrag (refereegranskat)abstract
    • This paper presents a vision-based approach to SLAM in large-scale environments with minimal sensing and computational requirements. The approach is based on a graphical representation of robot poses and links between the poses. Links between the robot poses are established based on odometry and image similarity, then a relaxation algorithm is used to generate a globally consistent map. To estimate the covariance matrix for links obtained from the vision sensor, a novel method is introduced based on the relative similarity of neighbouring images, without requiring distances to image features or multiple view geometry. Indoor and outdoor experiments demonstrate that the approach scales well to large-scale environments, producing topologically correct and geometrically accurate maps at minimal computational cost. Mini-SLAM was found to produce consistent maps in an unstructured, large-scale environment (the total path length was 1.4 km) containing indoor and outdoor passages.
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9.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • Non-iterative Vision-based Interpolation of 3D Laser Scans
  • 2007
  • Ingår i: Autonomos Agents and Robots. - Berlin/Heidelberg, Germany : Springer. - 9783540734239 ; , s. 83-90
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • 3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern colour camera. In this chapter we focus on methods to derive a highresolution depth image from a low-resolution 3D range sensor and a colour image. The main idea is to use colour similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to colour or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov random fields. The proposed algorithms are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data.
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10.
  • Andreasson, Henrik, 1977-, et al. (författare)
  • Vision based interpolation of 3D laser scans
  • 2006
  • Ingår i: Proceedings of the Third International Conference on Autonomous Robots and Agents. ; , s. 455-460
  • Konferensbidrag (refereegranskat)abstract
    • 3D range sensors, particularly 3D laser range scanners, enjoy a rising popularity and are used nowadays for many different applications. The resolution 3D range sensors provide in the image plane is typically much lower than the resolution of a modern color camera. In this paper we focus on methods to derive a high-resolution depth image from a low-resolution 3D range sensor and a color image. The main idea is to use color similarity as an indication of depth similarity, based on the observation that depth discontinuities in the scene often correspond to color or brightness changes in the camera image. We present five interpolation methods and compare them with an independently proposed method based on Markov Random Fields. The algorithms proposed in this paper are non-iterative and include a parameter-free vision-based interpolation method. In contrast to previous work, we present ground truth evaluation with real world data and analyse both indoor and outdoor data. Further, we suggest and evaluate four methods to determine a confidence measure for the accuracy of interpolated range values.
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