Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

By Todd Lupton, Salah Sukkarieh. IEEE Transactions on robotics, 2012

464 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Geology

View Paper: https://www.semanticscholar.org/paper/2295fda13d5118808557d04310b0e176a1341063

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Citations in the corpus

Contact-aided invariant extended Kalman filtering for robot state estimation (2019, background): “(2018) to potentially improve IMU preintegration (Eckenhoff et al., 2018; Forster et al., 2017; Lupton and Sukkarieh, 2012) as well as contact preintegration (Hartley et al.”
High-precision, consistent EKF-based visual-inertial odometry (2013, methodology): “…while several algorithms (especially in the GPSaided inertial navigation community) employ the simple approximation I I + F t that is equivalent to using one-step Euler integration (e.g. (Foxlin, 2005; Farrell, 2008; Zachariah and Jansson, 2010; Lupton and Sukkarieh, 2012; Vu et al., 2012)).”
Visual-Inertial Monocular SLAM With Map Reuse (2016, methodology): “2 gW∆t 2 + 1 2 RWB ( aB − ba ) ∆t(2) (2) The motion between two consecutive keyframes can be defined in terms of the preintegration ∆R, ∆v and ∆p from all measurements in-between [11].”
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator (2017, methodology): “IMU Pre-integration was ﬁrst proposed in [22], which parametrized rotation error in Euler angle.”
Using Inertial Sensors for Position and Orientation Estimation (2017, unknown): “n, we will focus on pose estimation using a camera and an IMU that are rigidly attached to each other. Inertial sensors and camera images have successfully been combined for pose estimation, see e.g. [18, 88, 68, 138, 78, 38, 13, 41, 105]. Application areas are for instance robotics, virtual reality”
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM (2020, methodology): “ORB-SLAM-VI [4] presented for the ﬁrst time a visual-inertial SLAM system able to reuse a map with short-term, mid-term and long-term data associations, using them in an accurate local visual-inertial BA based on IMU preintegration [61], [62].”
A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry (2018, methodology): “In this paper, we use the preintegrated IMU measurements proposed in [9], [10].”
Factor Graphs for Robot Perception (2017, methodology): “To deal with the typically large update rates of IMUs, one can use the idea by Lupton and Sukkarieh [143] to pre-integrate IMU measurements between lower-rate measurements from other sensors such as cameras and LIDARs.”
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, methodology): “An overview of our approach is given in Section IV. The ﬁrst step towards this goal is the development of a novel preintegration theory. The use of preintegrated IMU measurements was ﬁrst proposed in [2] and consists of combining many inertial measurements between two keyframes into a single relativ”
Legged Robot State-Estimation Through Combined Forward Kinematic and Preintegrated Contact Factors (2017, methodology): “The high-dynamic motion and noise characterization is captured using preintegration of high-frequency sensors such as Inertial Measurement Units (IMUs) [18].”
Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight (2017, methodology): “In [26], datasets are collected using stereo visual inertial configuration with cameras running at 6.”
Visual-Inertial Navigation: A Concise Review (2019, background): “Lupton and Sukkarieh [33] ﬁrst developed the IMU preintegration, a computationally efﬁcient alternative to the standard inertial measurement integration, which peforms the discrete integration of the inertial measurement dynamics in a local frame of reference, thus preventing the need to reintegrate”
PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features (2018, background): “zbibj = [αbibj , βbibj , qbibj ] > are called pre-integration measurements [22] and can be calculated directly without knowing the body states of bi, which means when body state is changed the re-propagation is not necessary.”