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In this paper we propose a study on landmark identification as a step towards a localization setup for real-world robotic swarms setup. In real world, landmark identification is often tackled as a place recognition problem through the use of computationally intensive Convolutional Neural Networks. However, the components of a robotic swarm usually have limited computational and sensing capabilities that allows only for the application of relatively shallow networks that results in large percentage of recognition errors. In a previous attempt of solving a similar setup - cooperative object recognition - the authors of [1] have demonstrated how the use of communication among a swarm and a naive Bayes classifier was able to substantially improve the correct recognition rate. An assumption of that paper not compatible with a swarm localization setup was that all swarm components would be looking at the same object. In this paper, we propose the use of a weighting factor to relapse this assumption. Through the use of simulation data, we show that our approach provides high recognition rates even in situations in which the robots would look at different objects.
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Swarm Localization Through Cooperative Landmark Identification
In this paper we propose a landmark-based map localization system for robotic swarms. The proposed system leverages the capabilities of a distributed landmark identification algorithm developed for robotic swarms presented in [1]. The output of the landmark identification consists of a vector of probabilities that each individual robot is looking at a particular landmark in the environment. In this work, this vector is used individually by each component of the swarm to feed the measurement update of a particle filter to estimate the robot location. The system was tested in simulation to validate its performance.
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- Award ID(s):
- 1952862
- PAR ID:
- 10346254
- Date Published:
- Journal Name:
- International Symposium Distributed Autonomous Robotic Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-92790-5_33
