Abstract
Abstract. Visual odometry (VO) is a technique applied to track the dynamic positioning and orientation of a moving platform with one or more cameras taking image sequences. The determination relies on the estimation of relative orientation parameters (ROPs) of time adjacent images. The idea of stereo VO to develop a dual-camera system is adopted in this study. By taking advantage of the calibrated stereo camera, this system is able to recover the true scale of relative translation without the need from additional sensors. However, the scale might not be very accurate, and the error also could exist in the orientation including rotation and translation due to environmental factors such as the illumination and texture. Therefore, the primary objective of this study is to find the optimized theory and method of stereo VO. Through the analysis of the geometric relationship of the time adjacent stereo image pairs, locally optimized network adjustment is developed to improve the accuracy of ROPs.The proposed network adjustment model is verified by the simulation data and experiment data both. ROPs are adopted as observations that would update the states of the image sequence further. Besides, exterior orientation parameters (EOPs) of the dual-camera system could be optimized obviously during the whole operation. In this study, it is worth mentioning that 3D coordinates of object points matched in each image pair are not necessary to be calculated. The conventional bundle adjustment is not adopted, but more accurate EOPs still have been generated automatically during the process.
Highlights
Visual Odometry (VO) is the technique of determining the egomotion including the dynamic positioning and orientation of a platform by using a visual system with one or more cameras (Scaramuzza and Fraundorfer, 2011)
Considering the aforementioned issues, this study aims to develop a dual-camera system to implement stereo VO
The random bias is added in all relative orientation parameters (ROPs)
Summary
Visual Odometry (VO) is the technique of determining the egomotion including the dynamic positioning and orientation of a platform by using a visual system with one or more cameras (Scaramuzza and Fraundorfer, 2011). A mobile mapping platform could be a land, aerial or underwater vehicle, which could even be an autonomous one (Bonin-Font et al, 2008). VO has been developed parallelly and named differently as Visual Navigation or Vision-based Navigation in mobile mapping, navigation and robot fields. A VO system may involve real-time or post-processing time series image with automated image matching and orientation retrieving computation to generate 3D trajectories of the image sequences
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