Raci-Net: Ego-vehicle Odometry Estimation in Adverse Weather Conditions

Autonomous driving systems are highly dependent on sensors like cameras, LiDAR, and inertial measurement units (IMU) to perceive the environment and estimate their motion. Among these sensors, perception-based sensors are not protected from harsh weather and technical failures. Although existing methods show robustness against common technical issues like rotational misalignment and disconnection, they often degrade when faced with dynamic environmental factors like weather conditions. To address these problems, this research introduces a novel deep learning-based motion estimator that integrates visual, inertial, and millimeter-wave radar data, utilizing each sensor strengths to improve odometry estimation accuracy and reliability under adverse environmental conditions such as snow, rain, and varying light. The proposed model uses advanced sensor fusion techniques that dynamically adjust the contributions of each sensor based on the current environmental condition, with radar compensating for visual sensor limitations in poor visibility. This work explores recent advancements in radar-based odometry and highlights that radar robustness in different weather conditions makes it a valuable component for pose estimation systems, specifically when visual sensors are degraded. Experimental results, conducted on the Boreas dataset, showcase the robustness and effectiveness of the model in both clear and degraded environments.