Multi-Sensor Fusion Based Indoor Localization and Navigation System for Dual-Ackerman Steering Mobile Robots

Simultaneous localization and mapping (SLAM) has been playing an important role in robotics perception, due to its ability to simultaneously localize the robot's pose and build maps of the surrounding environment. Concurrently, path planning algorithms are widely utilized to achieve autonomous and efficient robot navigation. However, there remains a lack of robust, flexible, and fully integrated systems that tightly combine both technologies for practical deployment in narrow indoor spaces. To address these issues, we develop a multi-sensor fusion-based indoor localization and navigation system, and propose a flexible motion control algorithm specifically tailored for dual-Ackermann steering vehicles. Real-world experimental results demonstrate that our method achieves robust indoor localization and mapping, while enabling highly flexible steering and accurate navigation. The average translational Euclidean error of the system's navigation remains at 6.8 cm.