<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.</p></abstract>

This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed for evaluation of different data registration strategies. The algorithm is composed of the iterative registration component, thus ICP (Iterative Closest Point), ICP (point to projection), ICP with semantic discrimination of points, LS3D (Least Square Surface Matching), NDT (Normal Distribution Transform) can be chosen. Loop closing is based on LUM and LS3D. The main research goal was to investigate the semantic discrimination of measured points that improve the accuracy of final map especially in demanding scenarios such as multi-level maps (e.g., climbing stairs). The parallel programming based nearest neighborhood search implementation such as point to point, point to projection, semantic discrimination of points is used. The 6DSLAM framework is based on modified 3DTK and PCL open source libraries and parallel programming techniques using NVIDIA CUDA. The paper shows experiments that are demonstrating advantages of proposed approach in relation to practical applications. The major added value of presented research is the qualitative and quantitative evaluation based on realistic scenarios including ground truth data obtained by geodetic survey. The research novelty looking from mobile robotics is the evaluation of LS3D algorithm well known in geodesy.

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Institute of Fundamental Technological Research

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This work concerns the study of 6DSLAM algorithms with an application of robotic mobile mapping systems. The architecture of the 6DSLAM algorithm is designed...

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Published Online: Sep 09, 2017

Page range: 275 - 295

DOI: https://doi.org/10.1515/fcds-2017-0014

Keywords
Mobile robot, mobile mapping system, Iterative Closest Point, Least Square Surface Matching, Normal Distribution Transform, LUM, 6DSLAM, CUDA

© 2017 Janusz Bedkowski et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Benchmark of 6D SLAM (6D Simultaneous Localisation and Mapping) Algorithms with Robotic Mobile Mapping Systems

Published Online: Sep 09, 2017

Page range: 275 - 295

DOI: https://doi.org/10.1515/fcds-2017-0014

KeywordsMobile robot, mobile mapping system, Iterative Closest Point, Least Square Surface Matching, Normal Distribution Transform, LUM, 6DSLAM, CUDA

© 2017 Janusz Bedkowski et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
Mobile robot, mobile mapping system, Iterative Closest Point, Least Square Surface Matching, Normal Distribution Transform, LUM, 6DSLAM, CUDA