The monocular camera is widely used in robots and unmanned vehicles system because it is low cost and easy to calibrate and identify. However, the depth lack of the monocular camera hinders positioning and determining the real size of obstacles in the unmanned vehicle system. To solve the problem, we propose a collaborative structure to accurately acquire the position of static or dynamic obstacles based on the partially observing information from multiple monocular cameras. After that, a reinforcement learning based obstacle avoidance algorithm is proposed for unmanned vehicles under an unknown environment. Specifically, we discuss the influence of obstacles' moving orientations on the performance of obstacles adaptive avoidance. Simulation results verify the feasibility of the proposed algorithm.