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Fast and accurate s...
Fast and accurate scan registration through minimization of the distance between compact 3D NDT Representations
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- Stoyanov, Todor, 1984- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems ( AASS )
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- Magnusson, Martin, 1977- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems ( AASS )
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- Lilienthal, Achim J., 1970- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems ( AASS )
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- Andreasson, Henrik, 1977- (författare)
- Örebro universitet,Institutionen för naturvetenskap och teknik,Centre for Applied Autonomous Sensor Systems ( AASS )
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(creator_code:org_t)
- 2012-09-24
- 2012
- Engelska.
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Ingår i: The international journal of robotics research. - : Sage Publications. - 0278-3649 .- 1741-3176. ; 31:12, s. 1377-1393
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Registration of range sensor measurements is an important task in mobile robotics and has received a lot of attention. Several iterative optimization schemes have been proposed in order to align three-dimensional (3D) point scans. With the more widespread use of high-frame-rate 3D sensors and increasingly more challenging application scenarios for mobile robots, there is a need for fast and accurate registration methods that current state-of-the-art algorithms cannot always meet. This work proposes a novel algorithm that achieves accurate point cloud registration an order of a magnitude faster than the current state of the art. The speedup is achieved through the use of a compact spatial representation: the Three-Dimensional Normal Distributions Transform (3D-NDT). In addition, a fast, global-descriptor based on the 3D-NDT is defined and used to achieve reliable initial poses for the iterative algorithm. Finally, a closed-form expression for the covariance of the proposed method is also derived. The proposed algorithms are evaluated on two standard point cloud data sets, resulting in stable performance on a par with or better than the state of the art. The implementation is available as an open-source package for the Robot Operating system (ROS).
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- point set registration; mapping; normal distributions transform
- Datavetenskap
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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