Collaborative Optimization of Multi Modal Sensing Fusion and Visual Navigation

With the rapid advancement of intelligent systems such as autonomous vehicles and drones, multi-modal sensing fusion has emerged as a pivotal approach to enhance the robustness and accuracy of visual navigation systems. Traditional single-sensor solutions, including GNSS, IMU, and vision-based methods, face inherent limitations such as signal interference, error accumulation, and sensitivity to lighting conditions. This study proposes a collaborative optimization framework that integrates multi-modal data (e.g., LiDAR, radar, RFID, and IMU) through spatiotemporal alignment, feature complementarity, and joint optimization. Innovations in fusion architectures—centralized (e.g., Extended Kalman Filter) and distributed (e.g., hierarchical decision fusion), achieving up to 40% localization error reduction. Deep learning techniques, such as multi-task neural networks, further enhance cross-modal feature distillation, improving 3D detection accuracy by 15% on KITTI datasets. Practical applications in autonomous driving, UAV navigation, and special environments demonstrate the system’s adaptability, with sub-meter positioning accuracy in GNSS-denied environments and decimeter-level precision in indoor SLAM. Challenges such as dynamic adaptability, heterogeneous data alignment, and edge computing optimization are discussed, alongside future directions including adversarial learning frameworks, lightweight model deployment, and cross-modal transfer learning. This research provides a comprehensive pathway to advance the reliability and applicability of multi-modal fusion systems in complex real-world scenarios.