NETWORK ADJUSTMENT OF AUTOMATIC RELATIVE ORIENTATION FROM IMAGE SEQUENCES

Abstract

Recently, Visual Odometry (VO) using cameras for navigation is known as an alternative solution in GNSS-hostile environments. VO is a process of estimating the egomotion based on consecutive frames captured by the camera. 3D Motion including the attitude and position can be described as the exterior orientation parameters (EOPs) in photogrammetry. The advantage of VO compared with wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. Since VO computes the camera path incrementally, the errors are accumulated as well according to the motion of each new frame-to-frame over time. That would cause the drift in the estimated trajectory compared to the real path. To solve this issue, this research proposes the network adjustment model based on relative orientation parameters (ROPs) for monocular VO. The fundamental idea originates from the traverse in the field of surveying. A traverse is a series of consecutive lines whose ends have been marked in the field and whose lengths and angles have been determined from observations. Consequently, ROPs are adopted as observations in the model that would update the states of image sequence furthermore. In this research, it is worth mentioning that the coordinates of object points are not necessary to be calculated, and more accurate ROPs are improved automatically during the process. In the future, VO with this proposed method could be integrated with GNSS/INS to a navigation system.