Visual-inertial structure from motion: Observability vs minimum number of sensors

Martinelli, Agostino

doi:10.1109/icra.2014.6906979

Cited by 10 publications

(17 citation statements)

References 21 publications

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“…There is substantial work on the observability properties given a particular combination of sensors or measurements (Martinelli, 2011;Weiss, 2012) or only using data from a reduced set of IMU axes (Martinelli, 2014). Global unobservability of yaw and position, as well as growing uncertainty with respect to an initial pose of reference are intrinsic to the visual-inertial estimation problem (Hesch et al, 2012b;Huang et al, 2013;Hesch et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Keyframe-based visual–inertial odometry using nonlinear optimization

Leutenegger

Lynen

Bosse

et al. 2014

The International Journal of Robotics Research

1,549

1,009

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Combining visual and inertial measurements has become popular in mobile robotics, since the two sensing modalities offer complementary characteristics that make them the ideal choice for accurate Visual-Inertial Odometry or Simultaneous Localization and Mapping (SLAM). While historically the problem has been addressed with filtering, advancements in visual estimation suggest that non-linear optimization offers superior accuracy, while still tractable in complexity thanks to the sparsity of the underlying problem. Taking inspiration from these findings, we formulate a rigorously probabilistic cost function that combines reprojection errors of landmarks and inertial terms. The problem is kept tractable and thus ensuring real-time operation by limiting the optimization to a bounded window of keyframes through marginalization. Keyframes may be spaced in time by arbitrary intervals, while still related by linearized inertial terms. We present evaluation results on complementary datasets recorded with our custom-built stereo visual-inertial hardware that accurately synchronizes accelerometer and gyroscope measurements with imagery. A comparison of both a stereo and monocular version of our algorithm with and without online extrinsics estimation is shown with respect to ground truth. Furthermore, we compare the performance to an implementation of a state-of-the-art stochasic cloning sliding-window filter. This competititve reference implementation performs tightly-coupled filtering-based visual-inertial odometry. While our approach declaredly demands more computation, we show its superior performance in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Keyframe-based visual–inertial odometry using nonlinear optimization

Leutenegger

Lynen

Bosse

et al. 2014

The International Journal of Robotics Research

1,549

1,009

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“…The key question of observability when the SLAM state is augmented with bias parameters has also received some attention. 37 The motion model can be a function of the terrain over which the robot travels. Changing motion models and timevarying biases introduce interesting challenges for both theoretical analysis and practical implementations.…”

Section: Observabilitymentioning

confidence: 99%

A critique of current developments in simultaneous localization and mapping

Huang

Dissanayake

2016

International Journal of Advanced Robotic Systems

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The number of research publications dealing with the simultaneous localization and mapping problem has grown significantly over the past 15 years. Many fundamental and practical aspects of simultaneous localization and mapping have been addressed, and some efficient algorithms and practical solutions have been demonstrated. The aim of this paper is to provide a critical review of current theoretical understanding of the fundamental properties of the SLAM problem, such as observability, convergence, achievable accuracy and consistency. Recent research outcomes associated with these topics are briefly discussed together with potential future research directions.

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“…In [10], [11] we investigated the observability properties of the Vi-SfM when the number of inertial sensors is reduced. We considered separately the cases of camera extrinsically calibrated and not calibrated.…”

Section: Introductionmentioning

confidence: 99%

“…The goal of this paper is to extend the analysis in [10], [11] by also including the extreme case of a single point feature when the camera is not extrinsically calibrated. The system is described in section II.…”

Section: Introductionmentioning

confidence: 99%

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Minimalistic sensor design in visual-inertial structure from motion

Martinelli

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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International audience— This paper presents a theoretical investigation in the framework of visual-inertial sensor fusion and the results here provided are the extension of our previous contribution in [10], [11]. The general goal of this research is to establish minimalistic visual-inertial sensors settings, which still provide full information even in the most challenging situations, i.e., in the case of unknown camera extrinsic calibration, unknown magnitude of the gravity, unknown inertial sensor bias and when only a single point feature is available. The investigation here provided allows us to conclude that, even in the case of a single point feature, the information provided by a sensor suit composed by a monocular camera and two inertial sensors (along two independent axes and where at least one is an accelerometer) is the same as in the case of a complete inertial measurement unit (i.e., when the inertial sensors consist of three orthogonal accelerometers and three orthogonal gyroscopes). To derive this result, an observability analysis of systems with only one and two inertial sensors is performed. This analysis requires to approach an open problem in control theory, called the Unknown Input Observability (UIO). In this paper we adopt the same method introduced in [10], [11] to solve this UIO problem. The method has been here improved in order to deal with systems more complex than the ones analyzed in [10], [11]. The paper also provides a general discussion on UIO and in particular on the proposed solution

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Visual-inertial structure from motion: Observability vs minimum number of sensors

Cited by 10 publications

References 21 publications

Keyframe-based visual–inertial odometry using nonlinear optimization

Keyframe-based visual–inertial odometry using nonlinear optimization

A critique of current developments in simultaneous localization and mapping

Minimalistic sensor design in visual-inertial structure from motion

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