Zhao Tong, Liu Jieyu, Li Zhuo. Visual odometry with three-stage local binocular BA[J]. Opto-Electronic Engineering, 2018, 45(11): 180244. doi: 10.12086/oee.2018.180244
Citation: Zhao Tong, Liu Jieyu, Li Zhuo. Visual odometry with three-stage local binocular BA[J]. Opto-Electronic Engineering, 2018, 45(11): 180244. doi: 10.12086/oee.2018.180244

Visual odometry with three-stage local binocular BA

    Fund Project: Supported by National Natural Science Foundation of China (61203007, 61304001)
  • In this paper, a three-stage local binocular BA (bundule adjustment) is proposed based on the ORB-SLAM2 algorithm, which is based on the large value of the initial value and the binocular camera model. In order to reduce the influence of cumulative error on 3D-2D matching in the uniform model, the ring matching mechanism is introduced to eliminate the mismatched again and match the key frame map point with the current frame 3D-2D projection. In the tracking part of the local map optimization phase, the normal frame between the two nearest key frames is also optimized as the local frame when the key phase is inserted into the key frame. KITTI data set experiments show that the three-stage local binocular beam method has more accurate 3D-2D matching compared with ORB-SLAM2, which improves the optimization constraint and improves the motion estimation and optimization precision.
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  • Overview: Visual odometry (VO) generally cascades single-frame motion estimation to obtain global navigation information of the camera, so that errors accumulate. In order to obtain globally consistent navigation results in large-scale complex environments, VSLAM based on graph optimization has become a research hotspot. ORB-SLAM2 is an open source algorithm proposed by Mur-Artal in 2016. The High computational efficiency and the ability to run in real time under CPU configuration make it can be used for Visual navigation of features such as map reconstruction, loop detection, and relocation in many scenes such as handheld carrier in indoor environment, aircraft in industrial environment, vehicles driven in urban environments and so on. The ORB-SLAM2 algorithm has a monocular, binocular and depth camera interface, and has been extensively researched on the basis of this. In this paper, a three-stage local binocular BA is proposed based on the ORB-SLAM2 algorithm, which is based on the large value of the initial value and the binocular camera model. On the basis of the ORB-LATCH feature proposed in, in order to reduce the influence of cumulative error on 3D-2D matching in the uniform model, the ring matching mechanism is introduced. Re-purifying the feature matching according to whether the ring matching constraint is satisfied, ensuring the correctness of the matching, to eliminate the mismatched again and match the key frame map point with the current frame 3D-2D projection. In the tracking of the local map phase, since the binocular camera requires a base distance of sufficient length to effectively cope with the driving environment of the vehicle, both the stereo matching and the three-dimensional reconstruction require accurate base distance parameters. Therefore, considering that the camera calibration parameters may change during the motion, we optimize the base distance of the binocular camera each time a key frame is inserted. In the local map optimization stage, the normal frames between the last two key frames are also included in the local frame for optimization, which provides a larger number of accurate local map points for the next camera pose tracking, and improves the accuracy of the camera pose tracking. Experiment with the algorithm under the KITTI data set. The result shows that the three-stage local binocular beam method has more accurate 3D-2D matching compared with ORB-SLAM2, which improves the optimization constraint and improves the motion estimation and optimization precision. In terms of real-time, the VO based on the algorithm in this paper meets the frame rate requirement of 10 Hz for the KITTI data set.

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