This paper introduces an advanced sensor data integration algorithm aimed at enhancing the navigation accuracy and reliability of autonomous snowmobiles in challenging environments. These environments, characterized by snow-covered terrains and limited GPS signal availability, present unique obstacles to traditional navigation systems. The proposed algorithm integrates data from multiple sensors, including RTK systems, inertial measurement units, GPS, and additional components of intelligent transport systems. By employing a Kalman filter, the algorithm adaptively weights and fuses data from these sources, reducing the impact of sensor noise and redundancy while ensuring robust state estimation and accurate trajectory prediction.
The experimental study was conducted at the Large Telescope Azimuthal site, where snowmobiles navigated through complex terrains, demonstrating the algorithm’s ability to maintain stable and accurate positioning. The results highlight significant improvements in trajectory precision compared to conventional methods, particularly in environments with poor signal conditions. This research underscores the importance of multi-sensor data fusion in achieving reliable and efficient autonomous navigation.
Future research directions include expanding the sensor suite with modalities such as radar and thermal imaging to enhance robustness under extreme weather conditions. Additionally, efforts will be made to optimize the algorithm for real-time applications, reducing latency and improving computational efficiency. These advancements are critical for developing safer and more reliable autonomous transport systems capable of operating effectively in the most demanding environments.
