基于附加参数模型的自检校光束法区域网平差算法运行结果对比

Comparison of additional parameter model-based self-calibration bundle block adjustment algorithms

像点偏移是影响光束法区域网平差结果精度的主要因素之一。克服平差过程中像点偏移的影响,以提高区域网平差精度,一直受到业界高度重视。当前,克服像点偏移的常规做法是借助特定的参数模型对其进行描述,构建基于附加参数模型的自检校光束法区域网平差模型,在区域网平差的同时求解引入的参数,进而实现对系统误差的补偿。Bauer模型、Ebner模型、Brown模型、光学畸变模型是现有算法中描述像点偏移的常用模型。为了对上述4种模型的系统误差进行验证,本文首先在经典的光束法区域网平差模型基础上分别设计了与4种模型相对应的自检校光束法区域网平差模型,并予以编程实现;然后通过实例对上述4种模型的算法运行结果进行了验证、比较及分析。试验结果表明,Ebner模型和Brown模型的引入可有效提高光束法区域网平差的精度;Bauer模型的引入对小型区域网平差精度的提升更加有效;光学畸变模型的引入对区域网平差结果的精度提升作用相对较小。

Image point offset is one of the main factors affecting the accuracy of bundle block adjustment. For a long time, how to overcome the influence of the offset in adjustment, and then improve the accuracy of the block adjustment, has received close attention and great importance from scholars in this industry. At present, the conventional method to overcome image point offset is to describe it with a specific parameter model, build an additional parameter model-based self-calibration bundle block adjustment and solve the introduced parameters while doing adjustment to realize the compensation of systematic error. Among the existing algorithms, Bauer model, Ebner model, Brown model and optical distortion model are common models to describe image point offset. In order to verify the effect on systematic error of the above four models in block adjustment, this paper designs self-calibration bundle block adjustment models corresponding to the four models based on classic bundle block adjustment, and implements them by programming. Finally, the algorithm results are verified, compared and analyzed through examples. Experiments show that introduction of Ebner model and Brown model can effectively improve the accuracy of bundle block adjustment;introduction of Bauer model is more effective in improving the accuracy of small block adjustment;introduction of optical distortion has a relatively little effect on improving block adjustment accuracy.