A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping

Takeuchi, Eijiro; Tsubouchi, Takashi

doi:10.1109/iros.2006.282246

Cited by 171 publications

(83 citation statements)

References 1 publication

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“…Magnusson et al [7] propose the use of a sequence of grids, each with a different resolution. Takeuchi and Tsubouchi [12] use a heterogeneous grid structure with larger cells further from the sensor in each scan. Kaminade et al [13] introduce varying amounts of blurring to the Gaussians in order to model different levels of confidence in the representation.…”

Section: D-ndtmentioning

confidence: 99%

On the accuracy of the 3D Normal Distributions Transform as a tool for spatial representation

Stoyanov

Magnusson

Almqvist

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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Abstract-The Three-Dimensional Normal Distributions Transform (3D-NDT) is a spatial modeling technique with applications in point set registration, scan similarity comparison, change detection and path planning. This work concentrates on evaluating three common variations of the 3D-NDT in terms of accuracy of representing sampled semi-structured environments. In a novel approach to spatial representation quality measurement, the 3D geometrical modeling task is formulated as a classification problem and its accuracy is evaluated with standard machine learning performance metrics. In this manner the accuracy of the 3D-NDT variations is shown to be comparable to, and in some cases to outperform that of the standard occupancy grid mapping model.

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Section: D-ndtmentioning

confidence: 99%

On the accuracy of the 3D Normal Distributions Transform as a tool for spatial representation

Stoyanov

Magnusson

Almqvist

et al. 2011

2011 IEEE International Conference on Robotics and Automation

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“…Ravi Kaushik, Jizhong Xiao*, William Morris and Zhigang Zhu preview or odometry [2], Angle Histogram [10]. It is known that these algorithms also fail to converge to a global minimum when there is a large rotation and translation transformation between two scans, which is our case between the ground robot and the wall-climbing robot (one scan inverted with respect to each other if the wall-climbing robot is on the ceiling).…”

Section: Laser Scan Registration Of Dual-robot System Using Visionmentioning

confidence: 99%

“…D laser scan registration is a widely studied research topic in robotics community [1][2][3]. Relative pose between two laser scans has been traditionally computed using the ICP algorithm and its variants [1,4].…”

Section: Introductionmentioning

confidence: 99%

3D Laser scan registration of dual-robot system using vision

Kaushik

Xiao

Morris

et al. 2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a novel technique to register a set of two 3D laser scans obtained from a ground robot and a wall-climbing robot which operates on the ceilings to construct a complete map of the indoor environment. Traditional laser scan registration methods like the Iterative Closest Point (ICP) algorithm will not converge to a global minimum without a good initial estimate of the transformation matrix. Our technique uses an overhead camera on the wall-climbing robot to keep line of sight with the ground robot and solves the Perspective Three Point (P3P) Problem to obtain the transformation matrix between the wall-climbing robot and the ground robot, which serves as a good initial estimate for the ICP algorithm to further refine the transformation matrix. We propose a novel particle filter algorithm to identify the real pose of the wall-climbing robot out of up to four possible solutions to P3P problem using Grunert's solution. The initial estimation ensures convergence of the ICP algorithm to a global minimum at all times. The simulation and experimental results indicate that the resulting composite laser map is accurate. In addition, the vision-based approach increases the efficiency by reducing the number of iterations of the ICP algorithm.

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“…The algorithm indicates the correct solution even in cases when the overlap is poor and having large interfering structures within two successive scans. It can not get trapped in local minima like the well known Iterated Closest Point (ICP) [2] or the 3D Normal Distributions Transform (NDT) [3]. It is furthermore able to consider intensity information which are becoming important in the latest technology of Laser Range Finders (LRF).…”

Section: Introductionmentioning

confidence: 99%

Spectral registration of volume data for 6-DOF spatial transformations plus scale

Bülow

Birk

2011

2011 IEEE International Conference on Robotics and Automation

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This paper presents a robust spectral based 6 Degrees Of Freedoms (DOF) registration method for 3D data which is in addition capable to determine the scaling of the registered object. The principle is based on decoupling 3D rotation from 3D translation by a corresponding resampling process of the spectral magnitude using a 3D Fast Fourier Transform (FFT), a S O(3) Fourier transform and an integrated Mellin transform for a scale determination. The registration is then subsequently carried out, first the registration of rotation is done, in a second step the scale is determined and finally the translation is calculated. Results are presented with generated test series on public available data and data recorded with Laser Range Finders (LRF). Furthermore one example of a scan pair recorded under realistic conditions showing the robustness of this method is given.

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A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping

Cited by 171 publications

References 1 publication

On the accuracy of the 3D Normal Distributions Transform as a tool for spatial representation

On the accuracy of the 3D Normal Distributions Transform as a tool for spatial representation

3D Laser scan registration of dual-robot system using vision

Spectral registration of volume data for 6-DOF spatial transformations plus scale

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