A fast bundle adjustment method based on track selection

Bundle adjustment is the core of the Structure from Motion algorithm, and it is also a very time-consuming part, in which redundant observations and initial parameter values with large errors increase the time consumption of the algorithm. In order to improve the efficiency of bundle adjustment, we propose an track selection method based on uniformity, accuracy, coverage and connectivity criteria. Firstly, we divide the space of tracks into several 3D grids. Secondly, we start from the grid with the largest number of tracks, and eliminate the redundant tracks with low connectivity and low accuracy in each grid, while ensuring the connectivity and coverage of tracks. Finally, we use a variety of experimental data to verify this algorithm. The result shows that the algorithm can delete a large number of redundant tracks, and effectively improve the efficiency of the bundle adjustment method on the premise of ensuring the accuracy. When the track retention rate is 0.4, the efficiency of the bundle adjustment method is increased by about 2 times, and the corresponding precision loss value is 0.026.

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Publication Details

Published
2024-09-13
DOI
https://doi.org/10.1117/12.3038009
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
0.00
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article

A fast bundle adjustment method based on track selection

Song Chen, Pingfan Xiong, Jinquan Hu, Luman Yang et al.
Robotics and Sensor-Based Localization
article

A fast bundle adjustment method based on track selection

Song Chen, Pingfan Xiong, Jinquan Hu, Luman Yang, Changwei Liu
article en

Abstract

Bundle adjustment is the core of the Structure from Motion algorithm, and it is also a very time-consuming part, in which redundant observations and initial parameter values with large errors increase the time consumption of the algorithm. In order to improve the efficiency of bundle adjustment, we propose an track selection method based on uniformity, accuracy, coverage and connectivity criteria. Firstly, we divide the space of tracks into several 3D grids. Secondly, we start from the grid with the largest number of tracks, and eliminate the redundant tracks with low connectivity and low accuracy in each grid, while ensuring the connectivity and coverage of tracks. Finally, we use a variety of experimental data to verify this algorithm. The result shows that the algorithm can delete a large number of redundant tracks, and effectively improve the efficiency of the bundle adjustment method on the premise of ensuring the accuracy. When the track retention rate is 0.4, the efficiency of the bundle adjustment method is increased by about 2 times, and the corresponding precision loss value is 0.026.

Wuhan University (CN), Future Earth (CA)
Openalex Percentile: Top 77%
Robotics and Sensor-Based Localization
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A fast bundle adjustment method based on track selection — Song Chen, Pingfan Xiong, et al. · (2024) | TGRS Research Map | TGRS