Constant-time monocular self-calibration

Keivan, Nima; Sibley, Gabe

doi:10.1109/robio.2014.7090561

Cited by 10 publications

(15 citation statements)

References 13 publications

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“…Unfortunately, the expensive information metric and optimization algorithm prevent its use on resource-constrained platforms. Similarly, Keivan and Sibley [25] maintain a database of the most informative images to calibrate the intrinsic parameters of a camera but use a more efficient entropy-based information metric for the selection. Nobre et al [26] extend the same framework to calibrate multiple sensors and more recently Nobre et al [27] also include the relative pose between an IMU and a camera.…”

Section: E Passive Observability-aware Calibration -Calibration On Imentioning

confidence: 99%

“…In our work, we take a similar approach to [24,25] but also consider inertial measurements and consequently collect informative segments instead of images. In contrast to the general method of [24], we use an approximation for the visual-inertial use-case and neglect any cross-terms between segments when evaluating their information content.…”

Section: E Passive Observability-aware Calibration -Calibration On Imentioning

confidence: 99%

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Observability-Aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation

Schneider

Cadena

et al. 2019

IEEE Sensors J.

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External effects such as shocks and temperature variations affect the calibration of visual-inertial sensor systems and thus they cannot fully rely on factory calibrations. Recalibrations performed on short user-collected datasets might yield poor performance since the observability of certain parameters is highly dependent on the motion. Additionally, on resourceconstrained systems (e.g mobile phones), full-batch approaches over longer sessions quickly become prohibitively expensive.In this paper, we approach the self-calibration problem by introducing information theoretic metrics to assess the information content of trajectory segments, thus allowing to select the most informative parts from a dataset for calibration purposes. With this approach, we are able to build compact calibration datasets either: (a) by selecting segments from a long session with limited exciting motion or (b) from multiple short sessions where a single sessions does not necessarily excite all modes sufficiently. Realworld experiments in four different environments show that the proposed method achieves comparable performance to a batch calibration approach, yet, at a constant computational complexity which is independent of the duration of the session.

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Section: E Passive Observability-aware Calibration -Calibration On Imentioning

confidence: 99%

Section: E Passive Observability-aware Calibration -Calibration On Imentioning

confidence: 99%

Observability-Aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation

Schneider

Cadena

et al. 2019

IEEE Sensors J.

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“…For these reasons we focus on estimating sensor extrinsics. For camera intrinsic self-calibration refer to [1,19]. The proposed FastCal algorithm can be divided into three major components, summarized in algorithm 1: 1) Selecting informative segments so as to bound the computation time.…”

Section: Methodsmentioning

confidence: 99%

“…In [1], the update criteria was if the entropy h candidate associated to the measurements D candidate was smaller than the worst scoring batch in D in f o by a certain margin, the segment was swapped into the informative segment queue and a new estimate for Θ was obtained by optimizing over all the measurements in the queue. This approach performs well, as shown in [19,1], however it causes an excessive number of estimations of the entire priority queue, every time a new candidate segment is swapped in. could be swapped into the priority queue in place of segment 2, however by waiting until time t = 5 we can instead swap in the candidate window at time t = 4, with much more information content.…”

Section: Informative Segment Selectionmentioning

confidence: 97%

FastCal: Robust Online Self-calibration for Robotic Systems

Nobre

Heckman

2020

Springer Proceedings in Advanced Robotics

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We propose a solution for sensor extrinsic self-calibration with very low time complexity, competitive accuracy and graceful handling of often-avoided corner cases: drift in calibration parameters and unobservable directions in the parameter space. It consists of three main parts: 1) information-theoretic based segment selection for constant-time estimation; 2) observability-aware parameter update through a rank-revealing decomposition of the Fischer information matrix; 3) drift-correcting self-calibration through the time-decay of segments. At the core of our FastCal algorithm is the loosely-coupled formulation for sensor extrinsics calibration and efficient selection of measurements. FastCal runs up to an order of magnitude faster than similar self-calibration algorithms 1 , making FastCal ideal for integration into existing, resource-constrained, robotics systems.

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“…In our work, we follow a similar approach and identify informative motion segments to build a sparser but complete calibration dataset. Similarly to the work of [12], we use the entropy to efficiently approximate the information content of segments but calibrate the full visual-inertial model instead of just a camera. Additionally, we extend the information measure and evaluate the informativeness of segments w.r.t.…”

Section: Related Workmentioning

confidence: 99%

Visual-inertial self-calibration on informative motion segments

Schneider

Burri

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Environmental conditions and external effects, such as shocks, have a significant impact on the calibration parameters of visual-inertial sensor systems. Thus longterm operation of these systems cannot fully rely on factory calibration. Since the observability of certain parameters is highly dependent on the motion of the device, using short data segments at device initialization may yield poor results. When such systems are additionally subject to energy constraints, it is also infeasible to use full-batch approaches on a big dataset and careful selection of the data is of high importance.In this paper, we present a novel approach for resource efficient self-calibration of visual-inertial sensor systems. This is achieved by casting the calibration as a segment-based optimization problem that can be run on a small subset of informative segments. Consequently, the computational burden is limited as only a predefined number of segments is used. We also propose an efficient information-theoretic selection to identify such informative motion segments. In evaluations on a challenging dataset, we show our approach to significantly outperform state-of-the-art in terms of computational burden while maintaining a comparable accuracy.

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Constant-time monocular self-calibration

Cited by 10 publications

References 13 publications

Observability-Aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation

Observability-Aware Self-Calibration of Visual and Inertial Sensors for Ego-Motion Estimation

FastCal: Robust Online Self-calibration for Robotic Systems

Visual-inertial self-calibration on informative motion segments

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