Complementary filtering approach to orientation estimation using inertial sensors only

Kubelka, Vladimír; Michal, R.

doi:10.1109/icra.2012.6224564

Cited by 46 publications

(28 citation statements)

References 9 publications

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“…To estimate the velocity of the robot, terrain adaptive odometry method [9] is used and combined with IMU data and information provided by the ICP using the Extended Kalman filter (EKF). The precise and stable pitch and roll angles are obtained using a complementary filter [23]. In addition to this information, currents in the flipper and the main track drives are used to provide the knowledge about the weight distribution and ground contacts.…”

Section: State Representation and Feature Selectionmentioning

confidence: 99%

Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Zimmermann

Zuzanek

Michal

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper we introduce the concept of Adaptive Traversability (AT), which we define as means of autonomous motion control adapting the robot morphology-configuration of articulated parts and their compliance-to traverse unknown complex terrain with obstacles in an optimal way. We verify this concept by proposing a reinforcement learning based AT algorithm for mobile robots operating in such conditions. We demonstrate the functionality by training the AT algorithm under lab conditions on simple EUR-pallet obstacles and then testing it successfully on natural obstacles in a forest. For quantitative evaluation we define a metrics based on comparison with expert operator. Exploiting the proposed AT algorithm significantly decreases the cognitive load of the operator.

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Section: State Representation and Feature Selectionmentioning

confidence: 99%

Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Zimmermann

Zuzanek

Michal

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…Thus, the results of the analysis might prove beneficial to anyone asking the fundamental question: What is actually the best way to implement the EKF? Therefore, objective of this paper lies in comparing four different approaches: a nonlinear model (NLM) [6] and an error model (ERM) [7], each with and without a complementary filter (CF) for attitude estimation [8], [9]. The performance of attitude estimation using the CF was thoroughly evaluated as part of our previous work in [8], including testing of various filters to cope with inertial signals strongly affected by vibration.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, objective of this paper lies in comparing four different approaches: a nonlinear model (NLM) [6] and an error model (ERM) [7], each with and without a complementary filter (CF) for attitude estimation [8], [9]. The performance of attitude estimation using the CF was thoroughly evaluated as part of our previous work in [8], including testing of various filters to cope with inertial signals strongly affected by vibration. In this paper, dead reckoning is represented by estimation of the six-degree-of-freedom (6-DOF) pose of the UGV, using the proprioceptive sensors only: odometry obtained from motor encoders and an inertial measurement unit (IMU) that consists of accelerometers and gyroscopes providing specific force and angular rate measurements [10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots

Šimánek

Michal

Kubelka

2015

IEEE/ASME Trans. Mechatron.

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101

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This paper presents evaluation of four different state estimation architectures exploiting the extended Kalman filter (EKF) for 6-DOF dead reckoning of a mobile robot. The EKF is a well proven and commonly used technique for fusion of inertial data and robot's odometry. However, different approaches to designing the architecture of the state estimator lead to different performance and computational demands. While seeking the best possible solution for the mobile robot, the nonlinear model and the error model are addressed, both with and without a complementary filter for attitude estimation. The performance is determined experimentally by means of precision of both indoor and outdoor navigation, including complex-structured environment such as stairs and rough terrain. According to the evaluation, the nonlinear model combined with the complementary filter is selected as a best candidate (reaching 0.8 m RMSE and average of 4% return position error (RPE) of distance driven) and implemented for real-time onboard processing during a rescue mission deployment. Index Terms-Complementary filter (CF), extended Kalman filter (EKF), urban search and rescue (USAR).

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“…The kinematics model is established by using odometry, equation (1).The mobile robot is assumed to be traveling on a flat surface and ignore the question of wheel slippage. To describe mobile robot's kinematics model in this paper, establish the global coordinates robot coordinates Fig.…”

Section: Kinematics Modelmentioning

confidence: 99%

“…Autonomous mobile robot's position determination has been the subject of intelligent field, if mobile robot know its position and heading angle timely, it is a prerequisite in executing given tasks [1].The recent progress in sensor technologies and increase in microcontroller unit's computational power brings great experimental platform for research on localization of autonomous robots [2].The usual method for position and heading angle estimation of a mobile robot is using only odometry. This method using the data obtained from the mobile sensor like encoder to estimate the change of the position of mobile robot with time.…”

Section: Introductionmentioning

confidence: 99%

Research on EKF-Based Localization Method of Tracked Mobile Robot

Qu¹,

Zhang²,

Hou³

et al. 2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. To estimate the position and heading angle of mobile robot precisely, an measurement variable estimation model was proposed to adapt any angle. Fusing the predictive value of odometry and measurement data of multiple sensors by the Extended Kalman Filtering (EKF) for reducing the accumulative error by using only traditional odometry. The proposed models is verified by Matlab simulation and experimental results.

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Complementary filtering approach to orientation estimation using inertial sensors only

Cited by 46 publications

References 9 publications

Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Adaptive Traversability of unknown complex terrain with obstacles for mobile robots

Evaluation of the EKF-Based Estimation Architectures for Data Fusion in Mobile Robots

Research on EKF-Based Localization Method of Tracked Mobile Robot

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