Abstract
Relying on static representations of the environment limits the use of mapping methods in most real-world tasks. Real-world environments are dynamic and undergo changes that need to be handled through map adaptation. In this work, an object-based pose graph is proposed to solve the problem of mapping in indoor dynamic environments with mobile robots. In contrast to state-of-the art methods where binary classifications between movable and static objects are used, we propose a new method to capture the probability of different objects over time. Object probability represents how likely it is to find a specific object in its previous location and it gives a quantification of how movable specific objects are. In addition, grouping object probabilities according to object class allows us to evaluate the movability of different object classes. We validate our object-based pose graph in real-world dynamic environments. Results in mapping and map adaptation with a real robot show efficient map maintenance through several mapping sessions and results in object classification according to movability show an improvement compared to binary classification.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.