Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 543-549, 2017
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W3/543/2017/
doi:10.5194/isprs-archives-XLII-2-W3-543-2017
 
23 Feb 2017
IMPROVING GOOGLE'S CARTOGRAPHER 3D MAPPING BY CONTINUOUS-TIME SLAM
A. Nüchter1,2, M. Bleier2, J. Schauer1,2, and P. Janotta3 1Informatics VII – Robotics and Telematics, Julius Maximilian University of Würzburg, Germany
2Zentrum für Telematik e.V., Würzburg, Germany
3Measurement in Motion GmbH, Theilheim, Germany
Keywords: SLAM, trajectory optimization, backpack, personal laser scanner, 3D point clouds Abstract. This paper shows how to use the result of Google's SLAM solution, called Cartographer, to bootstrap our continuous-time SLAM algorithm. The presented approach optimizes the consistency of the global point cloud, and thus improves on Google’s results. We use the algorithms and data from Google as input for our continuous-time SLAM software. We also successfully applied our software to a similar backpack system which delivers consistent 3D point clouds even in absence of an IMU.
Conference paper (PDF, 5191 KB)


Citation: Nüchter, A., Bleier, M., Schauer, J., and Janotta, P.: IMPROVING GOOGLE'S CARTOGRAPHER 3D MAPPING BY CONTINUOUS-TIME SLAM, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W3, 543-549, doi:10.5194/isprs-archives-XLII-2-W3-543-2017, 2017.

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