Object-based pose graph for dynamic indoor environments

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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.
Mapping, Dynamics, Service robots
Bibliographic citation
Gomez, C., Hernandez, A. C., Derner, E., Barber, R. & Babuska, R. (2020). Object-Based Pose Graph for Dynamic Indoor Environments. IEEE Robotics and Automation Letters, 5(4), 5401–5408.