Browsing by Author "Bernardino, Alexandre"
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- 6D Visual Odometry with Dense Probabilistic Egomotion EstimationPublication . Silva, Hugo Miguel; Bernardino, Alexandre; Silva, EduardoWe present a novel approach to 6D visual odometry for vehicles with calibrated stereo cameras. A dense probabilistic egomotion (5D) method is combined with robust stereo feature based approaches and Extended Kalman Filtering (EKF) techniques to provide high quality estimates of vehicle’s angular and linear velocities. Experimental results show that the proposed method compares favorably with state-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.
- Combining sparse and dense methods in 6D Visual OdometryPublication . Silva, Hugo Miguel; Silva, Eduardo; Bernardino, AlexandreVisual Odometry is one of the most powerful, yet challenging, means of estimating robot ego-motion. By grounding perception to the static features in the environment, vision is able, in principle, to prevent the estimation bias rather common in other sensory modalities such as inertial measurement units or wheel odometers. We present a novel approach to ego-motion estimation of a mobile robot by using a 6D Visual Odometry Probabilistic Approach. Our approach exploits the complementarity of dense optical flow methods and sparse feature based methods to achieve 6D estimation of vehicle motion. A dense probabilistic method is used to robustly estimate the epipolar geometry between two consecutive stereo pairs; a sparse feature stereo approach to estimate feature depth; and an Absolute Orientation method like the Procrustes to estimate the global scale factor. We tested our proposed method on a known dataset and compared our 6D Visual Odometry Probabilistic Approach without filtering techniques against a implementation that uses the well known 5-point RANSAC algorithm. Moreover, comparison with an Inertial Measurement Unit (RTK-GPS) is also performed, for providing a more detailed evaluation of the method against ground-truth information.