ICRA 2011 Paper Abstract


Paper WeP205.6

Stoyanov, Todor (Learning Systems Lab, Center for Applied Autonomous Sensor Syste), Magnusson, Martin (Örebro University), Almqvist, Håkan (Learning Systems Lab, Center for Applied Autonomous Sensor Syste), Lilienthal, Achim, J. (Örebro University)

On the Accuracy of the 3D Normal Distributions Transform As a Tool for Spatial Representation

Scheduled for presentation during the Regular Sessions "SLAM IV" (WeP205), Wednesday, May 11, 2011, 16:40−16:55, Room 3G

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on July 14, 2020

Keywords Mapping, Range Sensing


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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