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—We present a new parametrization for point features within monocular simultaneous localization and mapping (SLAM) that permits efficient and accurate representation of uncertainty during undelayed initialization and beyond, all within the standard extended Kalman filter (EKF). The key concept is direct parametrization of the inverse depth of features(More)
Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object detectors from real-world web videos known only to contain objects of a target class. We propose a fully automatic pipeline that localizes objects in a set of videos of the class and learns a detector for(More)
— Recent work has shown that the probabilistic SLAM approach of explicit uncertainty propagation can succeed in permitting repeatable 3D real-time localization and mapping even in the 'pure vision' domain of a single agile camera with no extra sensing. An issue which has caused difficulty in monocular SLAM however is the initialization of features, since(More)
— Recently it has been shown that an inverse depth parametrization can improve the performance of real-time monocular EKF SLAM, permitting undelayed initialization of features at all depths. However, the inverse depth parametriza-tion requires the storage of 6 parameters in the state vector for each map point. This implies a noticeable computing overhead(More)
— Recently, classical pairwise Structure From Motion (SfM) techniques have been combined with non-linear global optimization (Bundle Adjustment, BA) over a sliding window to recursively provide camera pose and feature location estimation from long image sequences. Normally called Visual Odometry, these algorithms are nowadays able to estimate with(More)
Random Sample Consensus (RANSAC) has become one of the most successful techniques for robust estimation from a data set that may contain outliers. It works by constructing model hypotheses from random minimal data subsets and evaluating their validity from the support of the whole data. In this paper we present a novel combination of RANSAC plus Extended(More)
SLAM has been to reduce the assumptions required. In this paper we move towards the logical conclusion of this direction by implementing a fully Bayesian Interacting Multiple Models (IMM) framework which can switch automatically between parameter sets in a dimensionless formulation of monocular SLAM. Remarkably, our approach of full sequential probability(More)
This paper explores the impact that landmark parametrization has in the performance of monocular, EKF-based, 6-DOF simultaneous localization and mapping (SLAM) in the context of undelayed landmark initialization. Undelayed initialization in monocular SLAM challenges EKF because of the combination of non-linearity with the large uncertainty associated with(More)