Navigation System
Navigation systems aim to enable autonomous agents, from robots to self-driving cars, to move safely and efficiently through their environments. Current research emphasizes improving robustness and reliability through techniques like sensor fusion (e.g., GPS-IMU integration, multi-modal sensor data), advanced control algorithms (e.g., Model Predictive Path Integral, Kalman filters), and machine learning models (e.g., neural networks for ETA prediction, object recognition for visually impaired navigation). These advancements are crucial for expanding the capabilities of autonomous systems in diverse applications, including healthcare, transportation, and assistive technologies for people with disabilities.
Papers
Know What You Don't Know: Consistency in Sliding Window Filtering with Unobservable States Applied to Visual-Inertial SLAM (Extended Version)
Daniil Lisus, Mitchell Cohen, James Richard Forbes
Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results
Sergey Levine, Dhruv Shah
Localization and Navigation System for Indoor Mobile Robot
Yanbaihui Liu