Performance evaluation of visual SLAM using several feature extractors

Visual simultaneous localization and mapping (SLAM) implementations must use feature extraction to reduce the dimensionality of image input, yet no comparison of feature extractors exists in the context of visual SLAM. This paper presents both a method for comparison of visual SLAM performance using several different feature extractors and the first experimental study using this method. Possible evaluation metrics are discussed and consistency testing and accumulated uncertainty are chosen to measure performance. Three feature extractors commonly used for visual SLAM are examined: the Harris corner detector, the Kanade-Lucas-Tomasi tracker, and the scale-invariant feature transform. All three are found to perform similarly in an indoor test environment, close to or within the limits of measurement. A modest scale change is handled without difficulty. We conclude that feature extractor choice is not significant in terms of visual SLAM performance and other criteria may be used to make the selection.