This paper integrates deep learning technology, object detection, pose estimation, and map-based path navigation to design and implement an efficient pallet automatic docking robotic system. To fulfill the stringent requirements of high-precision pallet docking in indoor environments, this study presents a novel perception-to-action navigation framework that tightly integrates perception, localization, planning, and control. In the perception stage, a real-time pallet detection and instance segmentation method based on YOLOv8-Seg is proposed. The segmentation results are further combined with the Random Sample Consensus algorithm and Principal Component Analysis to improve the robustness and accuracy of pallet pose estimation. In the navigation stage, the proposed framework seamlessly switches between a macro-navigation phase and a micro-alignment phase. During macro-navigation, a SLAM-generated occupancy map is employed for environmental modeling, Dijkstra’s algorithm is utilized for global path planning, and a localization framework combining Adaptive Monte Carlo Localization and the Extended Kalman Filter is adopted to mitigate cumulative localization drift. Moreover, the Dynamic Window Approach provides real-time local obstacle avoidance and motion control. During micro-alignment, a vision-based Dynamic Visual Servoing scheme continuously incorporates pallet pose priors estimated by the perception module into the local control loop. This enables the mobile robot to actively compensate for lateral and angular errors during the final docking maneuver, thereby ensuring centimeter-level localization accuracy and robust pallet engagement performance. The effectiveness of the proposed framework is validated through rigorous physical experiments, demonstrating its ability to achieve superior docking accuracy and smooth material transportation.