Seo, M.-W., & Kia, S. S. (2024). Bayesian Online Learning for Human-assisted Target Localization. arXiv preprint arXiv:2308.11839v4.
This research paper proposes a novel method for dynamic target localization that combines data from autonomous sensors (like UAVs with cameras) and human operators providing spatial information through freehand sketches. The objective is to improve localization accuracy by effectively incorporating and adapting to the inherent uncertainty in human inputs.
The researchers develop a joint Bayesian learning framework that utilizes a particle-based Hidden Markov Model (HMM) for target localization. They introduce a probabilistic observation model for human drawings that considers both the reliability of human detection and its inherent uncertainty. This model uses a Beta distribution to represent human detection reliability, which is updated online using a computationally efficient moment-matching method.
The proposed method demonstrates superior performance compared to solely autonomous sensor-based localization, particularly in scenarios with sensor failures or faulty measurements. Simulation results show that even a limited number of human observations can significantly enhance localization accuracy. The adaptive nature of the model allows it to effectively capture and adjust to changes in human operator reliability over time.
This research highlights the significant value of human-machine collaboration in autonomous systems for dynamic target localization. By explicitly modeling and adapting to human reliability, the proposed method effectively leverages human insights to compensate for limitations in autonomous sensor data. The computationally efficient Bayesian learning framework enables real-time adaptation and integration of human inputs, making it suitable for time-critical applications.
This work contributes to the field of human-robot interaction by providing a robust and efficient framework for integrating human input in complex tasks like target localization. The proposed method has potential applications in various domains, including search and rescue, surveillance, and environmental monitoring.
The current research focuses on a 2D target localization scenario. Future work could explore extending the framework to 3D environments and incorporating more complex human input modalities beyond simple sketches. Additionally, investigating the impact of different human factors, such as expertise and cognitive load, on the model's performance could further enhance its robustness and applicability.
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