Vision and IMU Data Fusion: Closed-Form Solutions for Attitude, Speed, Absolute Scale, and Bias Determination

By Agostino Martinelli. IEEE Transactions on robotics, 2012

322 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/b6cfdcc52cbdd4275d3004a1b7edd5fe55fa2fb7

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (2015, background): “A thorough analysis of VIO observability properties is given in [1, 15, 19].”
Vision-Controlled Micro Flying Robots: From System Design to Autonomous Navigation and Mapping in GPS-Denied Environments (2014, background): “A non-linear observability analysis [27] reveals that all state variables are observable, including the inter-sensor calibration parameters pi and q s i .”
Quaternion kinematics for the error-state Kalman filter (2015, methodology): “…with many software libraries of widespread use in robotics, such as Eigen, ROS, Google Ceres, and with a vast amount of literature on Kalman filtering for attitude estimation using IMUs (Chou, 1992; Kuipers, 1999; Piniés et al., 2007; Roussillon et al., 2011; Martinelli, 2012, and many others).”
Automatic re-initialization and failure recovery for aggressive flight with a monocular vision-based quadrotor (2015, unknown): “978-1-4799-6923-4/15/$31.00 ©2015 IEEE 1722”
Optic-Flow Based Control of a 46g Quadrotor (2013, background): “along a straight line) are sufficient to provide observability [7] and where this condition does not have to be fulfilled continuously.”
Using Inertial Sensors for Position and Orientation Estimation (2017, unknown): “For pose estimation, inertial sensors are often combined with measurements from for instance a global navigation satellite system (GNSS) [71, 147, 59], an ultrawideband (UWB) system [74, 132, 110, 27, 30] or cameras [26, 61, 79, 94].”
Symbiotic human-robot collaborative assembly (2019, methodology): “The approach of vision and inertial data fusion was investigated by Martinelli [153], which consists of a monocular camera, three orthogonal accelerometers and orthogonal gyroscopes.”
Visual-Inertial Navigation: A Concise Review (2019, methodology): “In particular, Martinelli [123] introduced a closed-form solution to the monocular visual-inertial initialization problem and later extended to the case where gyroscope bias calibration is also included [125] as well as to the cooperative scenario [126].”