Egomotion Estimation and Reconstruction with Kalman Filters and GPS Integration

This paper presents an approach for egomotion estimation over stereo image sequences combined with extra GPS data. The accuracy of the estimated motion data is tested with 3D roadside reconstruction. Our proposed method follows the traditional flowchart of many visual odometry algorithms: it firstly establishes the correspondences between the keypoints of every two frames, then it uses the depth information from the stereo matching algorithms, and it finally computes the best description of the cameras’ motion. However, instead of simply using keypoints from consecutive frames, we propose a novel technique that uses a set of augmented and selected keypoints, which are carefully tracked by a Kalman filter fusion. We also propose to use the GPS data for each key frame in the input sequence, in order to reduce the positioning errors of the estimations, so that the drift errors could be corrected at each key frame. Finally, the overall growth of the build-up errors can be bounded within a certain range. A least-squares process is used to minimise the reprojection error and to ensure a good pair of translation and rotation measures, frame by frame. Experiments are carried out for trajectory estimation, or combined trajectory and 3D scene reconstruction, using various stereo-image sequences.