Search

In the process of 3D reconstruction of the target scene with the moving vehicle-borne single-line scanner, a method of monocular visual positioning is used to locate the exact position of the moving scanner. Attitude sensor, camera and the laser scanner are located on the same platform which is installed on the vehicle. Data can be collected synchronously. We obtained a top view according to the observation of camera's attitude by IPM. A series of planform are acquired at different moment. After dealing with these pictures with operations of extraction Speeded Up Robust Features (SURF), matching characteristic points and RANSAC algorism, we calculated the transformation matrix between two adjacent vertical views. The position and direction of the scanner at this moment can be calculated. On this basis, depending on the sensors' synchronization control algorithm, the original point clouds data got by laser scanner can be registered as a reference to the traveling track of the moving scanner, and then the 3D reconstruction of the target scene is established. Experiment result shows that this method is easy to operate, time-efficient, low cost, and the accuracy of the method in reconstructing 3D scene is demonstrated.

The monocular vision odometry simplifies the hardware and the software as opposed to the stereo vision odometry, but it also has defect. When the vehicle is in motion, the camera's attitude changes inevitably, what lead that the method's performance degrades. To solve this problem, we proposed a monocular visual odometry based on the inverse perspective mapping (IPM). Attitude of the camera is monitored in real time by the attitude sensor when the vehicle is moving. Then the images of road surface photographed by camera became top view by using the IPM algorithm, after that, the characters of images can be calculated by the Speeded Up Robust Features (SURF) algorithm. By the random sample consensus (RANSAC) algorithm, the amounts of translation and rotation between two adjacent images can be concluded. Accordingly, the movement distance and the course of the vehicle can be worked out. In order to test the ranging accuracy of the method, both static and dynamic experiments were implemented. Static experiment showed that the average accuracy of ranging of this method achieved 1.6%. Dynamic experiment showed that the ranging accuracy achieved 6%, and the heading measurement error is less than 1.3°. Therefore, the method proposed in this paper is easy to operate, time-efficient, low cost, and the accuracy of the method in ranging and heading measurement are demonstrated.