Abstract
Autonomous mobile robots need to acquire surrounding environmental information based on which they perform their self-localizations. Current autonomous mobile robots often use point cloud data acquired by laser range finders (LRFs) instead of image data. In the virtual robot autonomous traveling tests we have conducted in this study, we have evaluated the robot’s self-localization performance on Normal Distributions Transform (NDT) scan matching. This was achieved using 2D and 3D point cloud data to assess whether they perform better self-localizations in case of using 3D or 2D point cloud data.
Sign-in/Register to access full text options 
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 callDisclaimer: 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.
