Towards Consistent Vision-Aided Inertial Navigation

Hesch, Joel A.; Kottas, Dimitrios G.; Bowman, Sean L.; Roumeliotis, Stergios I.

doi:10.1007/978-3-642-36279-8_34

Cited by 59 publications

(93 citation statements)

References 31 publications

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“…The optimal A * , as shown in [9], can be determined by solving its KKT optimality condition [3], whose solution is:…”

Section: Observability-constrained Ekfmentioning

confidence: 99%

“…position and orientation (pose) of a sensing platform within GPS-denied environments, vision-aided inertial navigation is one of the most established, primarily due to its high precision and low cost. During the past decade, VINS have been successfully applied to spacecraft [20], automotive [17], and personal localization [9], demonstrating realtime performance.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps

Guo

Kottas

DuToit³

et al. 2014

Robotics: Science and Systems X

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Abstract-In order to develop Vision-aided Inertial Navigation Systems (VINS) on mobile devices, such as cell phones and tablets, one needs to consider two important issues, both due to the commercial-grade underlying hardware: (i) The unknown and varying time offset between the camera and IMU clocks (ii) The rolling-shutter effect caused by CMOS sensors. Without appropriately modelling their effect and compensating for them online, the navigation accuracy will significantly degrade. In this work, we introduce a linear-complexity algorithm for fusing inertial measurements with time-misaligned, rolling-shutter images using a highly efficient and precise linear interpolation model. As a result, our algorithm achieves a better accuracy and improved speed compared to existing methods. Finally, we validate the superiority of the proposed algorithm through simulations and real-time, online experiments on a cell phone.

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“…The optimal A * , as shown in [9], can be determined by solving its KKT optimality condition [3], whose solution is:…”

Section: Observability-constrained Ekfmentioning

confidence: 99%

Section: Introduction and Related Workmentioning

confidence: 99%

Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps

Guo

Kottas

DuToit³

et al. 2014

Robotics: Science and Systems X

View full text Add to dashboard Cite

show abstract

“…In this system, we perform the observability analysis and show that while the key results of the previous observability analyses (e.g., [8,13,15,16]) are valid (the robot's global position and its orientation around the normal of the plane are unobservable), by constraining visual observations to be on a horizontal plane, the orthogonal translation of the camera with respect to the plane becomes observable. More specifically, we prove that by observing unknown feature points on a horizontal plane, the navigation system has only three unobservable directions corresponding to the global translations parallel to the plane, and the rotation around the gravity vector.…”

Section: Introductionmentioning

confidence: 99%

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

Panahandeh

Hutchinson

Händel

et al. 2015

J Intell Robot Syst

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In this paper, we address the problem of ego-motion estimation by fusing visual and inertial information. The hardware consists of an inertial measurement unit (IMU) and a monocular camera. The camera provides visual observations in the form of features on a horizontal plane. Exploiting the geometric constraint of features on the plane into visual and inertial data, we propose a novel closed form measurement model for this system. Our first contribution in this paper is an observability analysis of the proposed planar-based visual inertial navigation system (VINS). In particular, we prove that the system has only three unobservable states corresponding to global translations parallel to the plane, and rotation around the gravity vector. Hence, compared to general VINS, an advantage of using features on the horizontal plane is that the vertical translation along the normal of the plane becomes observable. As the second contribution, we present a state-space formulation for the pose estimation in the analyzed system and solve it via a modified unscented Kalman filter (UKF). Finally, the findings of the theoretical analysis and 6-DoF motion estimation are validated by simulations as well as using experimental data.

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“…The rich representation of a scene captured in an image, together with the accurate short-term estimates by gyroscopes and accelerometers present in a typical IMU have been acknowledged to complement each other, with great uses in airborne [6,20] and automotive [14] navigation. Moreover, with the availability of these sensors in most smart phones, there is great interest and research activity in effective solutions to visual-inertial SLAM.…”

Section: Introductionmentioning

confidence: 99%

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

Leutenegger¹,

Furgale²,

Rabaud³

et al. 2013

Robotics: Science and Systems IX

339

248

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Abstract-The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non-linear optimization approaches for tasks such as visual Simultaneous Localization And Mapping (SLAM), following the discovery that this comes with significant advantages in quality of performance and computational complexity. Following this trend, we present a novel approach to tightly integrate visual measurements with readings from an Inertial Measurement Unit (IMU) in SLAM. An IMU error term is integrated with the landmark reprojection error in a fully probabilistic manner, resulting to a joint non-linear cost function to be optimized. Employing the powerful concept of 'keyframes' we partially marginalize old states to maintain a bounded-sized optimization window, ensuring real-time operation. Comparing against both vision-only and loosely-coupled visual-inertial algorithms, our experiments confirm the benefits of tight fusion in terms of accuracy and robustness.

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Towards Consistent Vision-Aided Inertial Navigation

Cited by 59 publications

References 31 publications

Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps

Efficient Visual-Inertial Navigation using a Rolling-Shutter Camera with Inaccurate Timestamps

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

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