Weighted Local Bundle Adjustment and Application to Odometry and Visual SLAM Fusion

Local Bundle Adjustments were recently introduced for visual SLAM (Simultaneous Localization and Mapping). In Monocular Visual SLAM, the scale factor is not observable and the reconstruction scale drifts as time goes by. On long trajectory, this problem makes absolute localisation not usable. To overcome this major problem, data fusion is a possible solution. In this paper, we describe Weighted Local Bundle Adjustment(WLBA) for monocular visual SLAM purposes. We show that W-LBA used with local covariance gives better results than Local Bundle Adjustment especially on the scale propagation. Moreover W-LBA is well designed for sensor fusion. Since odometer is a common sensor and is reliable to obtain a scale information, we apply W-LBA to fuse visual SLAM with odometry data. The method performance is shown on a large scale sequence.