Closed-form solution for absolute scale velocity estimation using visual and inertial data with a sliding least-squares estimation

Lippiello, Vincenzo; Mebarki, Rafik

doi:10.1109/med.2013.6608881

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…They showed that the nonlinearity of the system mainly arises only from rotation drift. Recent results suggest that by assuming the orientation is known, VINS may be solved in a linear closed form [23]- [27]. It has been shown that both the initial gravity vector and the body frame velocity can be estimated linearly.…”

Section: Introductionmentioning

confidence: 99%

“…This sliding window model applies to both linear initialization (Section V) and nonlinear optimization (Section VI). measurements in a sliding window [24]- [27] do not scale well to a large number of IMU measurements since they rely on double integration of accelerometer output over an extended period of time. Moreover, these closed-form approaches do not take the noise characteristic of the system into account, producing suboptimal results.…”

Section: Introductionmentioning

confidence: 99%

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Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration

Yang

Shen

2017

IEEE Trans. Automat. Sci. Eng.

221

105

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There have been increasing demands for developing microaerial vehicles with vision-based autonomy for search and rescue missions in complex environments. In particular, the monocular visual-inertial system (VINS), which consists of only an inertial measurement unit (IMU) and a camera, forms a great lightweight sensor suite due to its low weight and small footprint. In this paper, we address two challenges for rapid deployment of monocular VINS: 1) the initialization problem and 2) the calibration problem. We propose a methodology that is able to initialize velocity, gravity, visual scale, and camera-IMU extrinsic calibration on the fly. Our approach operates in natural environments and does not use any artificial markers. It also does not require any prior knowledge about the mechanical configuration of the system. It is a significant step toward plugand-play and highly customizable visual navigation for mobile robots. We show through online experiments that our method leads to accurate calibration of camera-IMU transformation, with errors less than 0.02 m in translation and 1°in rotation. We compare out method with a state-of-the-art marker-based offline calibration method and show superior results. We also demonstrate the performance of the proposed approach in largescale indoor and outdoor experiments.Note to Practitioners-This paper presents a methodology for online state estimation in natural environments using only a camera and a low-cost micro-electro-mechanical systems (MEMS) IMU. It focuses on addressing the problems of online estimator initialization, sensor extrinsic calibration, and nonlinear optimization with online refinement of calibration parameters. This paper is particularly useful for applications that have superior size, weight, and power constraints. It aims for rapid deployment of robot platforms with robust state estimation capabilities with ). This paper has supplementary downloadable multimedia material available at http://ieeexplore.ieee.org provided by the authors. The Supplementary Material contains the following. Three experiments are presented in the video to demonstrate the performance of our self-calibrating monocular visualinertial state estimation method. The first experiment details the camera-IMU extrinsic calibration process in a small indoor experiment. The second experiment evaluates the performance of the overall system in a large-scale indoor environment with highlights to the online calibration process. The third experiment presents the state estimation results in a large-scale outdoor environment using different camera configurations. This material is 52.6 MB in size. almost no setup, calibration, or initialization overhead. The proposed method can be used in platforms including handheld devices, aerial robots, and other small-scale mobile platforms, with applications in monitoring, inspection, and search and rescue.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration

Yang

Shen

2017

IEEE Trans. Automat. Sci. Eng.

221

105

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“…The vehicle initial position with respect to the fixed world frame is (−5, 5, 6) m, while the desired position is (0, 0, 1) m, thus requiring UAV's autonomous motion of at least 5 m. Note that no measure of the UAV translational velocity is used. The observer described by (10) provides an estimate of the velocity to controller (32) that generates desired thrust and attitude angles to the vehicle's native inner-loop block controller. The involved tuning gains are empirically set as follows: c 1 = 0.4, c 2 = 8, λ = 2, k 2o = 20, K 2o = 1e-2I 3 , and α 2 = 0.2.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For instance, one can set Γ = −(c 3 + c 1 c 2 )δ 3 , with c 3 being a positive control gain. In both the simulations and real-hardware experiments presented in the following, the vehicle is controlled with command (32) and (10). The visual controller generates roll/pitch angles and thrust to the UAV autopilot, as presented in Section III-B.…”

Section: Angular Velocity As Control Inputmentioning

confidence: 99%

“…Work [9] proposes a closed-form solution to extract the translational velocity of an IMU-attached camera, such that the IMU and vision data obtained through three positions are fused. A similar approach but adopting spherical visual features to compute the velocity of quadrotors equipped with a camera and IMU are proposed in [10]. In [11]- [14], IMU accelerometer readings are used in nonlinear observers for quadrotor velocity, pose, and pose and velocity estimation, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Nonlinear Visual Control of Unmanned Aerial Vehicles in GPS-Denied Environments

2015

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In this paper, we propose a nonlinear controller that stabilizes unmanned aerial vehicles in GPS-denied environments with respect to visual targets by using only onboard sensing. The translational velocity of the vehicle is estimated online with a nonlinear observer, which exploits spherical visual features as the main source of information. With the proposed solution, only four visual features have shown to be enough for the observer to operate in a real scenario. In addition, the observer is computationally light with constant numerical complexity, involving small-dimension matrices. The observer output is then exploited in a nonlinear controller designed with an integral backstepping approach, thus yielding a novel robust control system. By means of Lyapunov analysis, the stability of the closed-loop system is proved. Extensive simulation and experimental tests with a quadrotor are carried out to verify the validity and robustness of the proposed approach. The control system runs fully onboard on a standard processor, and only a low-cost sensing suite is employed. Tracking of a target whose speed exceeds 2 m/s is also considered in the real-hardware experiments.Index Terms-Image-based visual servoing, nonlinear controller, nonlinear observer, unmanned aerial vehicle (UAV), velocity estimation.

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Image‐Based Control for Aerial Manipulation

Mebarki¹,

Lippiello²

2014

Asian Journal of Control

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This paper presents a new image-based servo control scheme that endows an unmanned aerial vehicle (UAV) equipped with a robotic arm with the capability of automatically positioning elements on target objects. Through a new formalism, the proposed visual servo scheme controls both the UAV and the manipulator simultaneously. It takes into account the whole system redundancy as well as the peculiarity of under-actuation related to rotary-wing crafts. While it controls the system at the velocity level, it makes use of the mobility afforded by the UAV and the dexterity inherent to robot manipulators. The case of large initial errors is explicitly addressed. Results of simulations are reported to verify the effectiveness of the proposed approach.

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Closed-form solution for absolute scale velocity estimation using visual and inertial data with a sliding least-squares estimation

Cited by 19 publications

References 27 publications

Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration

Monocular Visual–Inertial State Estimation With Online Initialization and Camera–IMU Extrinsic Calibration

Nonlinear Visual Control of Unmanned Aerial Vehicles in GPS-Denied Environments

Image‐Based Control for Aerial Manipulation

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