There are some situations in which the landmarks used in simultaneous localization and mapping (SLAM) have their own classes and for ceiling view (CV)-based navigation, this is usually the case. Ceilings in the home or the office have circular landmarks, such as lamps, speakers, fire alarms, smoke alarms, and so on, but to our knowledge, their classes have not been fully exploited in the data association of SLAM. In this paper, a new SLAM method that exploits the class of the landmarks is proposed and is applied to ceiling view-based SLAM (cvSLAM). The fact that the landmark classification cannot always be correct is also taken into account in the new SLAM and is formulated in the FastSLAM framework. Finally, simulations and experiments are conducted and the validity of the proposed method is demonstrated.
Bibliographical noteFunding Information:
This work was supported by the ‘Cognitive model-based global localization for indoor robots’ project (number: 10031687) of the Ministry of Knowledge Economy, Republic of Korea.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications