ABSTRACT In response to the rapid change of attitude in complex situations such as sharp turns and camera jitter, visual odometry/visual simultaneous localization and mapping (VO/VSLAM) often requires a high camera frame rate. However, the high frame rate of the camera poses a great challenge to real-time VO/VSLAM, especially when the vehicle travels along a relatively flat trajectory which requires higher realtime performance. Therefore, in this paper, we propose an automatic method for key-frame selection based on the motion state of the vehicle, which can reduce data redundancy and improve the real-time performance and robustness of VO/VSLAM. First, a pyramid-layered Kanade-Lucas-Tomasi (KLT) algorithm is used to track the feature points, and the five-point method and RANSAC are used to calculate the essential matrix. Second, two-step decomposition is used to calculate the change of inter-frame attitude, and then, key-frames are automatically selected based on the motion state of the vehicle predicted by attitude change within a short time interval. To evaluate the method, we conduct extensive experiments on real data and Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset based on monocular visual odometry. Then, we made qualitative and quantitative evaluations from the comparison with the reference trajectory, relative error (RE), root mean square error (RMSE), and absolute trajectory error (ATE). The results indicate that our method can improve the real-time performance while ensuring accuracy, with the data redundancy reduced by about 40%-60%. In addition, the performance of our method is further verified by comparison with the current representative ORB-SLAM. INDEX TERMS VO/VSLAM, key-frame selection, motion state, two-step decomposition, attitude change.
Lin REF proposed an automatic method for key-frame selection based on the motion state of a vehicle.
An Automatic Key-Frame Selection Method for Monocular Visual Odometry of Ground Vehicle
IEEE Access
IEEE Access
Computer Science