In response to the challenges of off-road and unknown outdoor environments, which lack rich prior information and exhibit highly variable road types without clear boundaries, the paper explores a method for detecting drivable areas based on road topological constraints. Firstly, in order to meet the requirements of global task planning, road skeletons are semi-automatically extracted from satellite images to construct a global road network and plan the global trajectory. Secondly, the current driving direction, which is derived from the road topology network and real-time positioning, serves as a strong constraint for the adaptive growth of the drivable area, ensuring that the extension of the drivable area aligns with the road patterns. Finally, a bayesian-based method is proposed for the fusion of LiDAR and visual data to enhance the robustness of drivable area detection. The experiments show that the proposed method significantly improves road pattern consistency and detection rate. The detection loss is 0.2‰. The average proportion factor and average divergence factor under straight road is 92.14% and 12.38%.The average proportion factor and average divergence factor under winding road is 85.46% and 20.75%.