The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2016 · 14 citations · 25 references
ComputingAutonomous SystemsComputer ScienceAutonomous VehiclesComputer VisionRobot VisionObject RecognitionPoint Cloud ProcessingPose EstimationRobot LocalizationRobotic VisionVisual LocalizationAutonomous Vehicle NavigationBundle AdjustmentDescriptor MatchingInterest Point DetectionKeypoint LocalizationKeypoint TrackingMobile MappingMobile Mapping Systems
Abstract. Vision based localization is widely investigated for the autonomous navigation and robotics. One of the basic steps of vision based localization is the extraction of interest points in images that are captured by the embedded camera. In this paper, SIFT and SURF extractors were chosen to evaluate their performance in localization. Four street view image sequences captured by a mobile mapping system, were used for the evaluation and both SIFT and SURF were tested on different image scales. Besides, the impact of the interest point distribution was also studied. We evaluated the performances from for aspects: repeatability, precision, accuracy and runtime. The local bundle adjustment method was applied to refine the pose parameters and the 3D coordinates of tie points. According to the results of our experiments, SIFT was more reliable than SURF. Apart from this, both the accuracy and the efficiency of localization can be improved if the distribution of feature points are well constrained for SIFT.
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Speeded-Up Robust Features (SURF)
Herbert Bay, Andreas Ess, Tinne Tuytelaars et al. · Computer Vision and Image Understanding · 2008 · 13.4K citations
FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION
Marius Muja, David Lowe · 2009 · 2.6K citations · Full text