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Hedenberg, Klas
Publications (6 of 6) Show all publications
Hedenberg, K. & Åstrand, B. (2016). 3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway. In: Roger Bostelman, Elena Messina (Ed.), Roger Bostelman, Elena Messina (Ed.), Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor. Paper presented at Workshop on Autonomous Industrial Vehicles - from Laboratory to the Factory Floor, Seattle, Washington, USA, May 26-30, 2015 (pp. 41-56). Paper presented at Workshop on Autonomous Industrial Vehicles - from Laboratory to the Factory Floor, Seattle, Washington, USA, May 26-30, 2015. West Conshohocken, PA: ASTM International
Open this publication in new window or tab >>3D Sensors on Driverless Trucks for Detection of Overhanging Objects in the Pathway
2016 (English)In: Autonomous Industrial Vehicles: From the Laboratory to the Factory Floor / [ed] Roger Bostelman, Elena Messina, West Conshohocken, PA: ASTM International, 2016, p. 41-56Chapter in book (Refereed)
Abstract [en]

Human-operated and driverless trucks often collaborate in a mixed work space in industries and warehouses. This is more efficient and flexible than using only one kind of truck. However, because driverless trucks need to give way to driven trucks, a reliable detection system is required. Several challenges exist in the development of such a system. The first is to select interesting situations and objects. Overhanging objects are often found in industrial environments (e.g., tines on a forklift). Second is choosing a system that has the ability to detect those situations. (The traditional laser scanner situated two decimetres above the floor does not detect overhanging objects.) Third is to ensure that the perception system is reliable. A solution used on trucks today is to mount a two-dimensional laser scanner on top and tilt the scanner toward the floor. However, objects at the top of the truck will be detected too late, and a collision cannot always be avoided. Our aim is to replace the upper two-dimensional laser scanner with a three-dimensional camera, structural light, or time-of-flight (TOF) camera. It is important to maximize the field of view in the desired detection volume. Hence, the sensor placement is important. We conducted laboratory experiments to check and compare the various sensors' capabilities for different colors, using tines and a model of a tine in a controlled industrial environment. We also conducted field experiments in a warehouse. Our conclusion is that both the tested structural light and TOF sensors have problems detecting black items that are non-perpendicular to the sensor. It is important to optimize the light economy—meaning the illumination power, field of view, and exposure time—in order to detect as many different objects as possible.

Place, publisher, year, edition, pages
West Conshohocken, PA: ASTM International, 2016
Series
American Society for Testing and Materials Special Technical Publications, ISSN 0066-0558 ; 1594
National Category
Robotics
Research subject
Production and Automation Engineering
Identifiers
urn:nbn:se:his:diva-12820 (URN)10.1520/STP159420150051 (DOI)000380525000003 ()2-s2.0-84978164198 (Scopus ID)978-0-8031-7633-1 (ISBN)978-0-8031-7634-8 (ISBN)
Conference
Workshop on Autonomous Industrial Vehicles - from Laboratory to the Factory Floor, Seattle, Washington, USA, May 26-30, 2015
Available from: 2016-08-25 Created: 2016-08-25 Last updated: 2018-03-28Bibliographically approved
Hedenberg, K. & Åstrand, B. (2009). A Trinocular Stereo System for Detection of Thin Horizontal Structures. In: Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008: . Paper presented at International Conference on Advances in Electrical and Electronics Engineering held at the World Congress on Engineering and Computer Sciences 2008, WCECS 2008, 22-24 October 2008, San Francisco, California, USA (pp. 211-218). IEEE Computer Society
Open this publication in new window or tab >>A Trinocular Stereo System for Detection of Thin Horizontal Structures
2009 (English)In: Advances in Electrical and Electronics Engineering - IAENG Special Edition of the World Congress on Engineering and Computer Science 2008, IEEE Computer Society, 2009, p. 211-218Conference paper, Published paper (Refereed)
Abstract [en]

Many vision-based approaches for obstacle detection often state that vertical thin structure is of importance, e.g. poles and trees. However, there are also problem in detecting thin horizontal structures. In an industrial case there are horizontal objects, e.g. cables and fork lifts, and slanting objects, e.g. ladders, that also has to be detected. This paper focuses on the problem to detect thin horizontal structures. We introduce a test apparatus for testing thin objects as a complement for the test pieces for human safety described in the European standard EN 1525 Safety of industrial trucks – Driverless trucks and their systems. The system uses three cameras, situated as a horizontal pair and a vertical pair, which makes it possible to also detect thin horizontal structures. A sparse disparity map based on edges and a dense disparity map is used to identify problems with a trinocular system. Both methods use the Sum of Absolute Difference to compute the disparity maps. Tests show that the proposed trinocular system detects all objects at the test apparatus. If a sparse or dense method is used is not critical. Further work will implement the algorithm in real time and verify it on a final system in many types of scenery.

Place, publisher, year, edition, pages
IEEE Computer Society, 2009
Keywords
AGV safety, Computer vision, Multiple cameras, Obstacle detection, Stereo vision
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3949 (URN)10.1109/WCECS.2008.33 (DOI)000275915300025 ()2-s2.0-70350528785 (Scopus ID)978-0-7695-3555-5 (ISBN)978-1-4244-3545-6 (ISBN)
Conference
International Conference on Advances in Electrical and Electronics Engineering held at the World Congress on Engineering and Computer Sciences 2008, WCECS 2008, 22-24 October 2008, San Francisco, California, USA
Available from: 2010-05-20 Created: 2010-05-20 Last updated: 2018-08-20Bibliographically approved
Ericson, S., Hedenberg, K. & Johansson, R. (2009). Information Fusion for Autonomous Robotic Weeding. In: Stefan Fischer, Erik Maehle, Rüdiger Reischuk (Ed.), INFORMATIK 2009: Im Focus das Leben. Paper presented at 39th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Im Focus das Leben, INFORMATIK 2009. 39th Annual Meeting of the German Informatics Society (GI): Focus on Life, INFORMATIK 2009, Lübeck, Germany, 28 September 2009 through 2 October 2009 (pp. 2461-2473). Köllen Druck + Verlag GmbH
Open this publication in new window or tab >>Information Fusion for Autonomous Robotic Weeding
2009 (English)In: INFORMATIK 2009: Im Focus das Leben / [ed] Stefan Fischer, Erik Maehle, Rüdiger Reischuk, Köllen Druck + Verlag GmbH , 2009, p. 2461-2473Conference paper, Published paper (Refereed)
Abstract [en]

Information fusion has a potential applicability to a multitude of differentapplications. Still, the JDL model is mostly used to describe defense applications.This paper describes the information fusion process for a robot removing weed ina field. We analyze the robotic system by relating it to the JDL model functions.The civilian application we consider here has some properties which differ from thetypical defense applications: (1) indifferent environment and (2) a predictable andstructured process to achieve its objectives. As a consequence, situation estimatestend to deal with internal properties of the robot and its mission progress (throughmission state transition) rather than external entities and their relations. Nevertheless, the JDL model appears useful for describing the fusion activities of the weeding robot system. We provide an example of how state transitions may be detected and exploited using information fusion and report on some initial results. An additional finding is that process refinement for this type of application can be expressed in terms of a finite state machine.

Place, publisher, year, edition, pages
Köllen Druck + Verlag GmbH, 2009
Series
Lecture Notes in Informatics, ISSN 1617-5468 ; Vol. P-154
National Category
Computer and Information Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3525 (URN)2-s2.0-84874333160 (Scopus ID)978-3-88579-248-2 (ISBN)
Conference
39th Jahrestagung der Gesellschaft fur Informatik e.V. (GI): Im Focus das Leben, INFORMATIK 2009. 39th Annual Meeting of the German Informatics Society (GI): Focus on Life, INFORMATIK 2009, Lübeck, Germany, 28 September 2009 through 2 October 2009
Available from: 2009-12-08 Created: 2009-12-08 Last updated: 2018-01-12Bibliographically approved
Hedenberg, K. & Åstrand, B. (2008). Obstacle detection for thin horizontal structures. In: World Congress on Engineering and Computer Science 2008 (pp. 689-693). IAENG
Open this publication in new window or tab >>Obstacle detection for thin horizontal structures
2008 (English)In: World Congress on Engineering and Computer Science 2008, IAENG , 2008, p. 689-693Conference paper, Published paper (Refereed)
Abstract [en]

 Abstract— Many vision-based approaches for obstacle detection often state that vertical thin structure is of importance, e.g. poles and trees. However, there are also problem in detecting thin horizontal structures. In an industrial case there are horizontal objects, e.g. cables and fork lifts, and slanting objects, e.g. ladders, that also has to be detected. This paper focuses on the problem to detect thin horizontal structures. The system uses three cameras, situated as a horizontal pair and a vertical pair, which makes it possible to also detect thin horizontal structures. A comparison between a sparse disparity map based on edges and a dense disparity map with a column and row filter is made. Both methods use the Sum of Absolute Difference to compute the disparity maps. Special interest has been in scenes with thin horizontal objects. Tests show that a trinocular system with the sparse dense method based on the Canny detector works better for the environments we have tested.

 

Place, publisher, year, edition, pages
IAENG, 2008
Keywords
Computer vision, Obstacle detection, Stereo vision, Thin structures
Identifiers
urn:nbn:se:his:diva-2409 (URN)978-988-98671-0-2 (ISBN)
Available from: 2008-12-05 Created: 2008-12-01 Last updated: 2017-11-27
Khammari, L., De Vin, L. & Hedenberg, K. (2004). Change detection algorithms for vision supported navigation of AVGs. In: : . Paper presented at 9th Mechatronic Forum International Conference, 2004 (pp. 251-259).
Open this publication in new window or tab >>Change detection algorithms for vision supported navigation of AVGs
2004 (English)Conference paper, Published paper (Other academic)
Identifiers
urn:nbn:se:his:diva-1520 (URN)
Conference
9th Mechatronic Forum International Conference, 2004
Available from: 2007-07-06 Created: 2007-07-06 Last updated: 2017-11-27Bibliographically approved
Hedenberg, K. & Baerveldt, A.-J. (2004). Stereo vision-based collision avoidance. In: Abdulkadir Erden, Bülent E. Platin, Memis Acar (Ed.), Conference proceedings - the 9th Mechatronics Forum international conference: August 30 - September 1, 2004, Ankara, Turkey. Paper presented at The 9th Mechatronics Forum international conference, August 30 - September 1, 2004, Ankara, Turkey (pp. 259-270).
Open this publication in new window or tab >>Stereo vision-based collision avoidance
2004 (English)In: Conference proceedings - the 9th Mechatronics Forum international conference: August 30 - September 1, 2004, Ankara, Turkey / [ed] Abdulkadir Erden, Bülent E. Platin, Memis Acar, 2004, p. 259-270Conference paper, Published paper (Other academic)
Abstract [en]

This paper investigates whether a stereo vision system based on points of interest is robust enough to detect obstacles for applications like a mobile robot in an industrial environment and for the visually impaired. Points of interest are extracted with a known method, called KLT. Two algorithms to solve the correspondence problem (Sum of Squared Difference and Variance Normalized Correlation) are used and evaluated as well as a combination of the two. An improvement is made if the two algorithms are combined. The tests show that stereo vision based on points of interest only can be used robustly for obstacle detection if there is enough texture on the obstacle. Otherwise too few points of interest on the object are detected and a reliable estimation of the distance to the object cannot be made.

Identifiers
urn:nbn:se:his:diva-1557 (URN)975-6707-13-5 (ISBN)
Conference
The 9th Mechatronics Forum international conference, August 30 - September 1, 2004, Ankara, Turkey
Note

Stereo vision for mobile robots

Available from: 2007-07-13 Created: 2007-07-13 Last updated: 2018-08-29Bibliographically approved
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