In this paper we present an improved color-based planar¯ducial marker system. Our framework provides precise and robust full 3D pose estimation of markers with superior accuracy when compared with many¯ducial systems in the literature, while color information encoding enables using over 65 000 distinct markers. Unlike most color-based¯ducial frameworks, which requires prior classi¯cation training and color calibration, ours can perform reliably under illumination changes, requiring but a rough white balance adjustment. Our methodology provides good detection performance even under poor illumination conditions which typically compromise other marker identi¯cation techniques, thus avoiding the evaluation of otherwise falsely iden-ti¯ed markers. Several experiments are presented and carefully analyzed, in order to validate our system and demonstrate the signi¯cant improvement in estimation accuracy of both position and orientation over traditional techniques.
. Overview of our fiducial marker framework: Each marker is a checkerboard with color cells that provides accurate three-dimensional pose estimation and tens of thousands of distinct markers.Abstract-In this paper we present a planar fiducial marker system to be used with color cameras. Our system provides precise and robust full 3D pose estimation of the markers with superior accuracy when compared with many fiducial systems, while color information is used to provide more than 65,000 distinct markers. In contrast with most color-based fiducial frameworks, ours requires no prior classification training nor color calibration other than a rough white balance adjustment and can perform reliably under illumination changes. Finally, we also provide means of detecting when poor illumination conditions will compromise marker identification, thus avoiding to evaluate a false marker identification. We present several experiments that show significant improvement in accuracy of estimation of both position and orientation when compared with traditional techniques.
Fig. 1. Registration of a partially illuminated scene, a hard task due to the lack of textural information. The cloud alignment was done using the proposed RGB-D descriptor, Binary Appearance and Shape Elements (BASE), that combines appearance and shape.Abstract-This work proposes a novel RGB-D feature descriptor called Binary Appearance and Shape Elements (BASE) that efficiently combines intensity and shape information to improve the discriminative power and enable an enhanced and faster matching process. The new descriptor is used to align a set of RGB point clouds to generate dense three dimensional models of indoor environments. We compare the performance of stateof-the-art feature descriptors with the proposed descriptor for scene alignment through the registration of multiple indoor textured depth maps. Experimental results show that the proposed descriptor outperforms the other approaches in computational cost, memory consumption and match quality. Additionally, experiments based on cloud alignment show that the BASE descriptor is suitable to be used in the registration of RBG-D data even when the environment is partially illuminated.
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