2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (2018)
Lake Tahoe, NV, USA
Mar 12, 2018 to Mar 15, 2018
This paper presents a novel method for visual-inertial odometry. The method is based on an information fusion framework employing low-cost IMU sensors and the monocular camera in a standard smartphone. We formulate a sequential inference scheme, where the IMU drives the dynamical model and the camera frames are used in coupling trailing sequences of augmented poses. The novelty in the model is in taking into account all the cross-terms in the updates, thus propagating the inter-connected uncertainties throughout the model. Stronger coupling between the inertial and visual data sources leads to robustness against occlusion and feature-poor environments. We demonstrate results on data collected with an iPhone and provide comparisons against the Tango device and using the EuRoC data set.
distance measurement, image sensors, image sequences, inertial navigation, inference mechanisms, probability, sensor fusion
A. Solin, S. Cortes, E. Rahtu and J. Kannala, "PIVO: Probabilistic Inertial-Visual Odometry for Occlusion-Robust Navigation," 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA, 2018, pp. 616-625.