Abstract
Most of the existed visual odometry methods cannot work in dynamic environments since the dynamic objects lead to wrong uncertain feature associations. In this paper, we involved a learning-based object classification front end to recognize and remove the dynamic object, and thereby ensure our ego-motion estimator's robustness in high dynamic environments. Moreover, we newly classify the environmental objects into static, movable and dynamic three classes. This processing not only enables the ego-motion estimation in the dynamic environment but also leads to clean and complete map-ping results. The experimental results indicate that the proposed method outperformed the other state-of-the-art SLAM solutions in both dynamic and static indoor environments.
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.
