Abstract: For the data processing delay and inaccurate detection problems of dynamic obstacle detection for laser sensor in outdoor environments, a dynamic obstacle detection and representation approach is proposed based on 3-dimensional laser sensor Velodyne and four-line laser sensor Ibeo. By analyzing and processing the data from Velodyne, this approach accomplishes detection and tracking of dynamic obstacles around the unmanned vehicle. For the sector region in front of unmanned vehicle with high accuracy requirements, this approach adopts confidence distance theory to achieve data fusion of the information processed by Velodyne and the output motion state information provided by Ibeo, significantly improves detection accuracy of obstacle motion state, and performs time-delay revision for the locations of dynamic obstacles based on the fusion result. At last, the occupancy locations of dynamic obstacles and static obstacles are distinguished and marked in the occupancy grid map. This approach can accurately detect the obstacle motion information in outdoor environments, eliminate positional deviation caused by sensor data processing delay and accurately represent the dynamic and static obstacles information in the environment with the occupancy grid map. This approach is applied to our self-developed unmanned vehicle. Large amount of experiments and the outstanding performance of our unmanned vehicle in the "Intelligent Vehicle Future Challenge of China" prove its reliability and accuracy.