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Keyframe-based SLAM has achieved great success in terms of accuracy, efficiency and scalability. However, due to parallax requirement and delay of map expansion, traditional keyframe-based methods easily encounter the robustness problem in the challenging cases especially for fast motion with strong rotation. For AR applications in practice, these challenging cases are easily encountered, since a...
We present an approach to estimate absolute scale in handheld monocular SLAM by simultaneously tracking the user's face with a user-facing camera while a world-facing camera captures the scene for localization and mapping. Given face tracking at absolute scale, two images of a face taken from two different viewpoints enable estimating the translational distance between the two viewpoints in absolute...
In this paper we propose a novel method called s-DVO for dense visual odometry using a probabilistic sensor noise model. In contrast to sparse visual odometry, where camera poses are estimated based on matched visual features, we apply dense visual odometry which makes full use of all pixel information from an RGB-D camera. Previously, t-distribution was used to model photometric and geometric errors...
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