Attitude, Linear Velocity and Depth Estimation of a Camera Observing a Planar Target Using Continuous Homography and Inertial Data

Hua, Minh‐Duc; Manerikar, Ninad; Hamel, Tarek; Samson, Claude

doi:10.1109/icra.2018.8460512

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…The 3D coordinates of the point-features from stereo images expressed in the camera frame (cam0) are transformed to the frame attached to the vehicle using the calibration matrix provided in the dataset. To remove matched point-feature outliers the technique proposed in [39] has been used by choosing the thresholds S = 30, D = 6.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements

Wang

Tayebi

2020

IEEE Trans. Automat. Contr.

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This paper considers the problem of attitude, position and linear velocity estimation for rigid body systems relying on landmark measurements. We propose two hybrid nonlinear observers on the matrix Lie group SE2(3), leading to global exponential stability. The first observer relies on fixed gains, while the second one uses variable gains depending on the solution of a continuous Riccati equation (CRE). These observers are then extended to handle biased angular velocity measurements. Both simulation and experimental results are presented to illustrate the performance of the proposed observers.

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Section: Resultsmentioning

confidence: 99%

Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements

Wang

Tayebi

2020

IEEE Trans. Automat. Contr.

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“…withλ ∈ B 3 2 equal to twice the vector part of the quaternion associated with the attitude error matrixR, and whose convergence to zero implies the convergence ofR to R. One then deduces from ( 10), (11), and the identity…”

Section: A Observer Equations and Model Adaptationmentioning

confidence: 97%

“…third subplot of Figure 3) during the time-interval (145 sec, 185 sec), the figure shows that the solvePnP() algorithm switches between two possible pitch angle solutions. [11] by adopting the proposed observer design framework. A monocular camera and an IMU provide the measurements needed for the estimation process.…”

Section: A Experimental Validationmentioning

confidence: 99%

Deterministic observer design for vision-aided inertial navigation

Hamel

Hua²,

Samson³

2020

2020 59th IEEE Conference on Decision and Control (CDC)

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The purpose of this tutorial is to report results developed over the last three years for state estimation problems arising with unmanned mobile robots equipped with a monocular camera and a 3-axis gyrometer, complemented with either a velocity sensor, or a 3-axis accelerometer, or an optical flow sensor. Definition and characterization of uniform observability for linear time-varying systems, followed by an observer design framework exploiting first-order approximations of a class of nonlinear systems, are first recalled. The resulting Riccati observers are locally uniformly exponentially stable when associated uniform observability conditions are satisfied. This framework is subsequently applied, with detailed explanations, to a set of practical problems, namely i) classical PnP camera pose estimation using known source points and bearing measurements, ii) the adaptation of this problem to unknown source points by using epipolar constraints, iii) camera pose and velocity estimation using bearing and IMU measurements, and iv) camera velocity and depth estimation using optical flow and IMU measurements from the observation of a planar target. The observer solutions proposed for these last two problems are validated with experimental data.

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“…Different from [11], [12], [18], our strategy to separately estimate the gravity direction and the plane's normal allows the proposed method to relax the assumption that camera observes horizontal ground. In other words, it is applicable to flights above an inclined plane.…”

Section: Flights Over Tilted Planesmentioning

confidence: 99%

“…In the implementation, the plane's normal was assumed aligned with the gravity vector that is absolutely determined rather than estimated by the IMU measurements. To address the shortcoming, Hua et al presented a nonlinear observer to estimate the depth, velocity, and gravity direction using the horizontal plane assumption [12]. The observer is unable to A sketch comparing the measurement model of the patch-based approach [16] with the proposed method: (a) the patch-based method in [16] and (b) the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Direct Visual-Inertial Ego-Motion Estimation Via Iterated Extended Kalman Filter

Zhong

Chirarattananon

2020

IEEE Robot. Autom. Lett.

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This letter proposes a reactive navigation strategy for recovering the altitude, translational velocity and orientation of Micro Aerial Vehicles. The main contribution lies in the direct and tight fusion of Inertial Measurement Unit (IMU) measurements with monocular feedback under an assumption of a single planar scene. An Iterated Extended Kalman Filter (IEKF) scheme is employed. The state prediction makes use of IMU readings while the state update relies directly on photometric feedback as measurements. Unlike feature-based methods, the photometric difference for the innovation term renders an inherent and robust data association process in a single step. The proposed approach is validated using real-world datasets. The results show that the proposed method offers better robustness, accuracy, and efficiency than a feature-based approach. Further investigation suggests that the accuracy of the flight velocity estimates from the proposed approach is comparable to those of two state-of-the-art Visual Inertial Systems (VINS) while the proposed framework is ≈ 15 − 30 times faster thanks to the omission of reconstruction and mapping.

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Attitude, Linear Velocity and Depth Estimation of a Camera Observing a Planar Target Using Continuous Homography and Inertial Data

Cited by 12 publications

References 23 publications

Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements

Hybrid Nonlinear Observers for Inertial Navigation Using Landmark Measurements

Deterministic observer design for vision-aided inertial navigation

Direct Visual-Inertial Ego-Motion Estimation Via Iterated Extended Kalman Filter

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