Abstract
A novel Laser-SLAM algorithm is presented for real indoor environment mobile mapping. SLAM algorithm can be divided into two classes, Bayes filter-based and graph optimization-based. The former is often difficult to guarantee consistency and accuracy in largescale environment mapping because of the accumulative error during incremental mapping. Graph optimization-based SLAM method often assume predetermined landmarks, which is difficult to be got in unknown environment mapping. And there most likely has large difference between the optimize result and the real data, because the constraints are too few. This paper designed a kind of sub-map method, which could map more accurately without predetermined landmarks and avoid the already-drawn map impact on agent’s location. The tree structure of sub-map can be indexed quickly and reduce the amount of memory consuming when mapping. The algorithm combined Bayes-based and graph optimization-based SLAM algorithm. It created virtual landmarks automatically by associating data of sub-maps for graph optimization. Then graph optimization guaranteed consistency and accuracy in large-scale environment mapping and improved the reasonability and reliability of the optimize results. Experimental results are presented with a laser sensor (UTM 30LX) in official buildings and shopping centres, which prove that the proposed algorithm can obtain 2D maps within 10cm precision in indoor environment range from several hundreds to 12000 square meter.
Highlights
In recent years, the technology of simultaneous localization and mapping (SLAM) has been applied in the surveying and mapping domain
Laser scanners are often used in high precision surveying and mapping, Laser range finder is extensively used to acquire 2D information from environment for its high abilities of prevent interference and low influence of light, and many laserSLAM algorithm have been applied to the robot, but most algorithms are only suitable for small scale and low precision mapping, the new problem with it is that how to draw high precision large-scale map
The SLAM problem is decomposed into two independent posterior probability:The posterior over maps p(m | x1:t, z1:t) and the posterior over potential trajectories p(x1:t | z1:t), which can be estimated by particle filter
Summary
The technology of simultaneous localization and mapping (SLAM) has been applied in the surveying and mapping domain. Laser scanners are often used in high precision surveying and mapping, Laser range finder is extensively used to acquire 2D information from environment for its high abilities of prevent interference and low influence of light, and many laserSLAM algorithm have been applied to the robot, but most algorithms are only suitable for small scale and low precision mapping, the new problem with it is that how to draw high precision large-scale map. SLAM algorithm is essentially a system state estimation problem In this way, the solving method can be roughly divided into three categories, which are based on Kalman filter, particle filter and based on graph theory. SLAM algorithm based on graph theory, due to the use of the global optimization of the processing method, can get a better mapping result
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
