Journal
IEEE ROBOTICS AND AUTOMATION LETTERS
Volume 5, Issue 4, Pages 5401-5408Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LRA.2020.3007402
Keywords
Mapping; dynamics; service robots
Categories
Funding
- HEROITEA: Heterogeneous Intelligent Multi-Robot Team for Assistance of Elderly People [RTI2018-095599-B-C21]
- Spanish Ministerio de Economia y Competitividad
- RoboCity2030-DIH-CM Project, RoboCity2030 -Madrid Robotics Digital Innovation Hub [S2018/NMT-4331]
- European Regional Development Fund under the project Robotics for Industry 4.0 [CZ.02.1.01/0.0/0.0/15_003/0000470]
- Grant Agency of the Czech Technical University in Prague [SGS19/174/OHK3/3T/13]
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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.
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