The Influence of the Spatial Distribution of 2-D Features on Pose Estimation for a Visual Pipe Mapping Sensor

This paper considers factors which influence the visual motion estimation of a sensor system designed for visually mapping the internal surface of pipework using omnidirectional lenses. In particular, a systematic investigation of the error caused by a non-uniform 2-D spatial distribution of features on the resultant estimate of camera pose is presented. The effect of non-uniformity is known to cause issue and is commonly mitigated using techniques, such as bucketing; however, a rigorous analysis of this problem has not been carried out in the literature. The pipe's inner surface tends to be uniform and texture poor driving the need to understand and quantify the feature matching process. A simulation environment is described in which the investigation was conducted in a controlled manner. Pose error and uncertainty is considered as a function of the number of correspondences and feature coverage pattern in the form of contiguous and equiangular coverage around a circular image acquired by a fisheye lens. It is established that beyond 16 feature matches between the images, that coverage is the most influential variable, with the equiangular coverage pattern leading to a greater rate of reduction in pose error with increasing coverage. The application of the results of the simulation to a real world data set is also provided.