Publication:
Object-based pose graph for dynamic indoor environments

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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.
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Keywords
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.