Sensitivity study for feature-based monocular 3D SLAM

Advances in cameras and computing hardware, both in terms of performance and miniaturization, has made vision-based localization a feasible sensor for aerial vehicles. In GPS deprived environments or scenarios where the resolution of GPS is not sufficient, such a sensor presents an attractive alternative. Vision-based position sensors typically estimate their pose by tracking natural features in the environment, while at the same time creating a map of those features. This process is referred to as simultaneous localization and mapping (SLAM), and it employs several sub-processes, such as feature detection and description, map generation, feature mapping, and optimization, each of which is subject to a large number of parameters. Due to the complexity of the problem, finding a satisfactory parameter setting can be a tedious task. In this paper we investigate the effects of each parameter in the context of SLAM. As an example we use the PTAM (Parallel Tracking and Mapping) algorithm from the University of Oxford. The results of this sensitivity study indicate which parameters are most influential in achieving good tracking performance and also show suitable ranges for each parameter. This information can be used to expedite discovery of a satisfactory parameter setting for a new environment.