A robust and easy to implement method for IMU calibration without external equipments

Tedaldi, David; Pretto, Alberto; Menegatti, Emanuele

doi:10.1109/icra.2014.6907297

Cited by 189 publications

(106 citation statements)

References 15 publications

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“…Compared to most existing reconstruction methods, our system builds models in real-time during operation: this is important in order to allow the robot to act autonomously and to allow for own decision-making based on the environment explored so far. We have devoted a considerable effort in making the approach more robust and developing an extension of DCS, originally proposed by Agarwal et al [3], to assessing the degree of consistency of maps [5] and to automatically calibrating the sensors (see also [6], [7]). In order to safely navigate in the environment, the robot uses an abstract 2D representation of the environment called traversability map [8] that, when coupled with exploration techniques that consider the expected gain of novel information (see [9] for an overview tutorial), allows for a safe and exhaustive survey of the environment.…”

Section: A the Digiro Robot And The Mission Control Interfacementioning

confidence: 99%

A new approach to digitalization and data management of cultural heritage sites

Ziparo¹,

Cottefoglie²,

Calisi³

et al. 2015

2015 Digital Heritage

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In this paper, we describe a novel approach for acquiring and managing digital models of archaeological sites. More in detail, we present an approach to digitization based on a robotic platform and a cloud-based information system. Our robot is the result of over two years of efforts by a group of cultural heritage experts, computer scientists and roboticists. Exploiting the large and heterogeneous amount data provided by the robotic platform requires this data to be managed, organized and analyzed. To this extent we developed ARIS (ARchaeological Information System), a software that exploits modern information retrieval and machine learning systems.

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Section: A the Digiro Robot And The Mission Control Interfacementioning

confidence: 99%

A new approach to digitalization and data management of cultural heritage sites

Ziparo¹,

Cottefoglie²,

Calisi³

et al. 2015

2015 Digital Heritage

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“…For continuous calibration, Kalman filtering techniques [2] and genetic algorithms [7] have been proposed to compensate for uncertainty about stimulus and frames of reference from a moving vehicle or gimbal, respectively. For multiposition calibration, which is more often proposed, a variety of techniques have been proposed for choosing inertial sensor orientations and reference stimuli and addressing uncertainties [8], [9]. However, neither the long duration velocity stimulus nor the large rotational displacements required by the above methods are easily achieved by a self-contained microsystem, and calibration duration is generally long, on the order of several minutes to an hour.…”

Section: Introductionmentioning

confidence: 99%

Estimation With Threshold Sensing for Gyroscope Calibration Using a Piezoelectric Microstage

Edamana

Chen

Slavin

et al. 2015

IEEE Trans. Contr. Syst. Technol.

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A sensing estimation scheme is presented that combines analog and threshold sensing on a piezoelectric microactuator for calibration of microscale inertial sensors. Using a variation of the Kalman filter, an asynchronous threshold sensor improves state estimates obtained from less reliable analog sensor measurements of microactuator motion. The resulting velocity estimates are compared with estimates without threshold sensing, and related to feasible calibration performance for gyroscopes. Results show that incorporating threshold sensors in a projected low-noise environment based on capacitive sensing will produce high-accuracy velocity measurements at certain fixed angles, with an approximately 80% reduction in angular velocity estimation error. Experimental testing with noisier, more variable piezoelectric sensing shows improved estimation accuracy at all velocities and positions when threshold detections are added. In simulation, the addition of feedback control is shown to further improve estimation accuracy.

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“…The accelerometer and gyroscope biases are estimated with the IMU-calibration algorithm suggested by Tedaldi et al [17]. However, we include also the biases in the state vector since we observe a difference between dynamic and static biases.…”

Section: Process Modelmentioning

confidence: 99%

Off-the-shelf sensor integration for mono-SLAM on smart devices

Tiefenbacher

Schulze

Rigoll

2015

2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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This work proposes a fusion of inertial measurement units (IMUs) and a visual tracking system on an embedded device. The sensor-to-sensor calibration and the pose estimation are both achieved through an unscented Kalman filter (UKF). Two approaches for a UKF-based pose estimation are presented: The first uses the estimated pose of the visual SLAM system as measurement input for the UKF; The second modifies the motion model of the visual tracking system. Our results show that IMUs increase tracking accuracy even if the visual SLAM system is untouched, while requiring little computational power. Furthermore, an accelerometer-based map scale estimation is presented and discussed.

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A robust and easy to implement method for IMU calibration without external equipments

Cited by 189 publications

References 15 publications

A new approach to digitalization and data management of cultural heritage sites

A new approach to digitalization and data management of cultural heritage sites

Estimation With Threshold Sensing for Gyroscope Calibration Using a Piezoelectric Microstage

Off-the-shelf sensor integration for mono-SLAM on smart devices

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