Abstract
Abstract. In order to quick response to the rapid changes of mobile platforms in complex situations such as speedy changing direction or camera shake, visual odometry/visual simultaneous localization and mapping (VO/VSLAM) always needs a high frame rate vision sensor. However, the high frame rate of the sensor will affect the real-time performance of the odometry. Therefore, we need to investigate how to make a balance between the frame rate and the pose quality of the sensor. In this paper, we propose an automatic key-frame method based on the improved PWC-Net for mobile platforms, which can improve the pose tracking quality of odometry, the error caused by dynamic blur and the global robustness. First, a two-step decomposition is used to calculate the change of inter-frame attitude, and then, key-frames are added by the improved PWC-Net or automatically selected based on the motion state of the vehicle predicted by pose change with a short time interval. To evaluate the method, we conduct extensive experiments on KITTI dataset based on monocular visual odometry. The results indicate that our method can keep the pose tracking quality while ensuring the real-time performance.
Highlights
With the development of unmanned aerial vehicle (UAV) technology, autonomous flying, high precision localization and mobile mapping in an unknown environment are important
With unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images, accurate, stable and well-distributed matches are difficult to obtain, real-time VO/VSLAM and largescale structure from motion pose severe challenges to limited computing resources (Chen et al, 2019)
KITTI datasets: In order to evaluate the performance of our key-frame selection method proposed in this paper, the KITTI dataset experiments were carried out respectively
Summary
With the development of unmanned aerial vehicle (UAV) technology, autonomous flying, high precision localization and mobile mapping in an unknown environment are important. With unpredictable texture structure and motion blur continuously exist in mobile platform visual imagery and seriously reduce the similarity between images, accurate, stable and well-distributed matches are difficult to obtain, real-time VO/VSLAM and largescale structure from motion pose severe challenges to limited computing resources (Chen et al, 2019). To address this problem, appropriate key-frame selection strategy can help increase the accuracy and consistency of local motion estimation of VO/VSLAM.
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