Abstract
At present, most feature-based SLAM methods are based on feature points. But in indoor environments where texture is scarce and light varies greatly, these methods will reduce the accuracy of pose estimation or even lead to pose tracking failure. To tackle the above problem, we propose an improved RGB-D SLAM method which employs point-line-plane feature extraction and matching as well as Manhattan Frames-based rotation estimation. Finally, the proposed method is proven to have good reliability and robustness in open datasets and real indoor scenes.
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.
