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  • 1.
    Ahtiainen, Juhana
    et al.
    Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Stoyanov, Todor
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Saarinen, Jari
    GIM Ltd., Espoo, Finland.
    Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments2017Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 34, nr 3, s. 600-621Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

  • 2.
    Almqvist, Håkan
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz Piotr
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Learning to detect misaligned point clouds2018Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 35, nr 5, s. 662-677Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Matching and merging overlapping point clouds is a common procedure in many applications, including mobile robotics, three-dimensional mapping, and object visualization. However, fully automatic point-cloud matching, without manual verification, is still not possible because no matching algorithms exist today that can provide any certain methods for detecting misaligned point clouds. In this article, we make a comparative evaluation of geometric consistency methods for classifying aligned and nonaligned point-cloud pairs. We also propose a method that combines the results of the evaluated methods to further improve the classification of the point clouds. We compare a range of methods on two data sets from different environments related to mobile robotics and mapping. The results show that methods based on a Normal Distributions Transform representation of the point clouds perform best under the circumstances presented herein.

  • 3.
    Grelsson, Bertil
    et al.
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan. Saab Dynamics, Linköping, Sweden.
    Felsberg, Michael
    Linköpings universitet, Institutionen för systemteknik, Datorseende. Linköpings universitet, Tekniska högskolan.
    Isaksson, Folke
    Vricon Systems, Saab, Linköping, Sweden.
    Highly Accurate Attitude Estimation via Horizon Detection2016Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, nr 7, s. 967-993Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Attitude (pitch and roll angle) estimation from visual information is necessary for GPS-free navigation of airborne vehicles. We propose a highly accurate method to estimate the attitude by horizon detection in fisheye images. A Canny edge detector and a probabilistic Hough voting scheme are used to compute an approximate attitude and the corresponding horizon line in the image. Horizon edge pixels are extracted in a band close to the approximate horizon line. The attitude estimates are refined through registration of the extracted edge pixels with the geometrical horizon from a digital elevation map (DEM), in our case the SRTM3 database, extracted at a given approximate position. The proposed method has been evaluated using 1629 images from a flight trial with flight altitudes up to 600 m in an area with ground elevations ranging from sea level up to 500 m. Compared with the ground truth from a filtered inertial measurement unit (IMU)/GPS solution, the standard deviation for the pitch and roll angle errors obtained with 30 Mpixel images are 0.04° and 0.05°, respectively, with mean errors smaller than 0.02°. To achieve the high-accuracy attitude estimates, the ray refraction in the earth's atmosphere has been taken into account. The attitude errors obtained on real images are less or equal to those achieved on synthetic images for previous methods with DEM refinement, and the errors are about one order of magnitude smaller than for any previous vision-based method without DEM refinement.

  • 4.
    Kleiner, Alexander
    et al.
    iRobot Corp, MA 01730 USA.
    Heintz, Fredrik
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten.
    Tadokoro, Satoshi
    Tohoku University, Japan.
    Editorial: Special Issue on Safety, Security, and Rescue Robotics (SSRR), Part 12016Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, nr 3, s. 263-264Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    n/a

  • 5.
    Kleiner, Alexander
    et al.
    iRobot Corp, MA 01730 USA.
    Heintz, Fredrik
    Linköpings universitet, Institutionen för datavetenskap, Artificiell intelligens och integrerade datorsystem. Linköpings universitet, Tekniska fakulteten.
    Tadokoro, Satoshi
    Tohoku University, Japan.
    Editorial: Special Issue on Safety, Security, and Rescue Robotics (SSRR), Part 22016Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, nr 4, s. 409-410Artikel i tidskrift (Övrigt vetenskapligt)
    Abstract [en]

    n/a

  • 6.
    Magnusson, Martin
    et al.
    Örebro universitet, Akademin för naturvetenskap och teknik.
    Andreasson, Henrik
    Örebro universitet, Akademin för naturvetenskap och teknik.
    Nüchter, Andreas
    Jacobs University Bremen.
    Lilienthal, Achim J.
    Örebro universitet, Akademin för naturvetenskap och teknik.
    Automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform2009Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 26, nr 11-12, s. 892-914Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We propose a new approach to appearance-based loop detection for mobile robots, usingthree-dimensional (3D) laser scans. Loop detection is an important problem in the simultaneouslocalization and mapping (SLAM) domain, and, because it can be seen as theproblem of recognizing previously visited places, it is an example of the data associationproblem. Without a flat-floor assumption, two-dimensional laser-based approaches arebound to fail in many cases. Two of the problems with 3D approaches that we address inthis paper are how to handle the greatly increased amount of data and how to efficientlyobtain invariance to 3D rotations.We present a compact representation of 3D point cloudsthat is still discriminative enough to detect loop closures without false positives (i.e.,detecting loop closure where there is none). A low false-positive rate is very important becausewrong data association could have disastrous consequences in a SLAM algorithm.Our approach uses only the appearance of 3D point clouds to detect loops and requires nopose information. We exploit the normal distributions transform surface representationto create feature histograms based on surface orientation and smoothness. The surfaceshape histograms compress the input data by two to three orders of magnitude. Becauseof the high compression rate, the histograms can be matched efficiently to compare theappearance of two scans. Rotation invariance is achieved by aligning scans with respectto dominant surface orientations. We also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determinethe threshold that separates scans at loop closures from nonoverlapping ones.Wediscuss the problem of determining ground truth in the context of loop detection and thedifficulties in comparing the results of the few available methods based on range information.Furthermore, we present quantitative performance evaluations using three realworlddata sets, one of which is highly self-similar, showing that the proposed methodachieves high recall rates (percentage of correctly identified loop closures) at low falsepositiverates in environments with different characteristics.

  • 7.
    Magnusson, Martin
    et al.
    Örebro universitet, Institutionen för teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för teknik.
    Duckett, Tom
    Department of Computing and Informatics, University of Lincoln, Lincoln, United Kingdom.
    Scan registration for autonomous mining vehicles using 3D-NDT2007Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 24, nr 10, s. 803-827Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalisation and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Straßer, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory-efficient scan surface representation.

  • 8.
    Mühlfellner, Peter
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Bürki, Mathias
    ETH, Zürich, Switzerland.
    Bosse, Mike
    ETH, Zürich, Switzerland.
    Derendarz, Wojciech
    Volkswagen AG, Wolfsburg, Germany.
    Philippsen, Roland
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Furgale, Paul
    ETH, Zürich, Switzerland.
    Summary Maps for Lifelong Visual Localization2016Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 33, nr 5, s. 561-590Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Robots that use vision for localization need to handle environments which are subject to seasonal and structural change, and operate under changing lighting and weather conditions. We present a framework for lifelong localization and mapping designed to provide robust and metrically accurate online localization in these kinds of changing environments. Our system iterates between offline map building, map summary, and online localization. The offline mapping fuses data from multiple visually varied datasets, thus dealing with changing environments by incorporating new information. Before passing this data to the online localization system, the map is summarized, selecting only the landmarks that are deemed useful for localization. This Summary Map enables online localization that is accurate and robust to the variation of visual information in natural environments while still being computationally efficient.

    We present a number of summary policies for selecting useful features for localization from the multi-session map and explore the tradeoff between localization performance and computational complexity. The system is evaluated on 77 recordings, with a total length of 30 kilometers, collected outdoors over sixteen months. These datasets cover all seasons, various times of day, and changing weather such as sunshine, rain, fog, and snow. We show that it is possible to build consistent maps that span data collected over an entire year, and cover day-to-night transitions. Simple statistics computed on landmark observations are enough to produce a Summary Map that enables robust and accurate localization over a wide range of seasonal, lighting, and weather conditions. © 2015 Wiley Periodicals, Inc.

  • 9.
    Ortiz Morales, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Westerberg, Simon
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    La Hera, Pedro
    Sveriges lantbruksuniversitet .
    Mettin, Uwe
    Department of Transmission and Hybrid Systems, IAV Automotive Engineering, Berlin, Germany.
    Freidovich, Leonid
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Shiriaev, Anton
    Norwegian University of Science and Technology.
    Increasing the level of automation in the forestry logging process with crane trajectory planning and control2014Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 31, nr 3, s. 343-363Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Working with forestry machines requires great amount of training to be sufficiently skilledto operate forestry cranes. In view of this, introducing automated motions, as those seenin robotic arms, is ambitioned by this industry for shortening the amount of training timeand make the work of the operator easier. Motivated by this fact, we have developedtwo experimental platforms for testing control systems and motion planning algorithms inreal-time. They correspond to a laboratory setup and a commercial version of a hydraulicmanipulator used in forwarder machines. The aim of this article is to present the results ofthis development by providing an overview of our trajectory planning algorithm and motioncontrol method, with a subsequent view of the experimental results. For motion control,we design feedback controllers that are able to track reference trajectories based on sensormeasurements. Likewise, we provide arguments to design controllers in open-loop for thecase of machines lacking of sensing devices. Relying on the tracking efficiency of thesecontrollers, we design time efficient reference trajectories of motions that correspond tologging tasks. To demonstrate performance, we provide an overview of an extensive testingdone on these machines.

  • 10.
    Stoyanov, Todor
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Comparative evaluation of the consistency of three-dimensional spatial representations used in autonomous robot navigation2013Ingår i: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 30, nr 2, s. 216-236Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An increasing number of robots for outdoor applications rely on complex three-dimensional (3D) environmental models. In many cases, 3D maps are used for vital tasks, such as path planning and collision detection in challenging semistructured environments. Thus, acquiring accurate three-dimensional maps is an important research topic of high priority for autonomously navigating robots. This article proposes an evaluation method that is designed to compare the consistency with which different representations model the environment. In particular, the article examines several popular (probabilistic) spatial representations that are capable of predicting the occupancy of any point in space, given prior 3D range measurements. This work proposes to reformulate the obtained environmental models as probabilistic binary classifiers, thus allowing for the use of standard evaluation and comparison procedures. To avoid introducing localization errors, this article concentrates on evaluating models constructed from measurements acquired at fixed sensor poses. Using a cross-validation approach, the consistency of different representations, i.e., the likelihood of correctly predicting unseen measurements in the sensor field of view, can be evaluated. Simulated and real-world data sets are used to benchmark the precision of four spatial models—occupancy grid, triangle mesh, and two variations of the three-dimensional normal distributions transform (3D-NDT)—over various environments and sensor noise levels. Overall, the consistency of representation of the 3D-NDT is found to be the highest among the tested models, with a similar performance over varying input data.

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