Quantitative Evaluation of Feature Extractors for Visual SLAM

We present a performance evaluation framework for visual feature extraction and matching in the visual simultaneous localization and mapping (SLAM) context. Although feature extraction is a crucial component, no qualitative study comparing different techniques from the visual SLAM perspective exists. We extend previous image pair evaluation methods to handle non-planar scenes and the multiple image sequence requirements of our application, and compare three popular feature extractors used in visual SLAM: the Harris corner detector, the Kanade-Lucas-Tomasi tracker (KLT), and the scale-invariant feature transform (SIFT). We present results from a typical indoor environment in the form of recall/precision curves, and also investigate the effect of increasing distance between image viewpoints on extractor performance. Our results show that all methods can be made to perform well, although it is possible to distinguish between the three. We conclude by presenting guidelines for selecting a feature extractor for visual SLAM based on our experiments.