State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and

Bloesch, Michael; Hutter, Marco; Hoepflinger, Mark A.; Leutenegger, Stefan; Gehring, Christian; Remy, C. David; Siegwart, Roland

doi:10.15607/rss.2012.viii.003

Cited by 163 publications

(261 citation statements)

References 15 publications

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“…In our first set of experiments, the system sequentially feeds footsteps to the Boston Dynamics walking controller (with its own internal state estimate) for execution 3 while continually modifying their positions during execution so that the controller achieves the intended motion. Figure 9 summarizes our results for a variety of walking patterns totaling 57 minutes of operation and 155 meters traveled.…”

Section: Methodsmentioning

confidence: 99%

“…An EKF-based estimator is presented in [3] for a quadruped that uses a sensor-based prediction model and creates filter corrections using foothold measurements. This approach incorporates the positions of footholds into the state vector (using a point model for each foot) and gives particular consideration to consistency and observability analysis.…”

Section: A Related Workmentioning

confidence: 99%

“…As with many leg kinematic integration algorithms [1], [3], our approach assumes that the robot's stance foot maintains non-slipping contact with the ground during part of the gait and that this foot is stationary. This allows instantaneous velocity and position measurements of the robot's pelvis to be inferred 1 The manufacturer-provided orientation estimate was not used but is compared with in Section VII.…”

Section: Leg Kinematicsmentioning

confidence: 99%

See 2 more Smart Citations

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Fallon

Antone

Roy

et al. 2014

2014 IEEE-RAS International Conference on Humanoid Robots

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Abstract-This paper describes an algorithm for the probabilistic fusion of sensor data from a variety of modalities (inertial, kinematic and LIDAR) to produce a single consistent position estimate for a walking humanoid. Of specific interest is our approach for continuous LIDAR-based localization which maintains reliable drift-free alignment to a prior map using a Gaussian Particle Filter. This module can be bootstrapped by constructing the map on-the-fly and performs robustly in a variety of challenging field situations. We also discuss a two-tier estimation hierarchy which preserves registration to this map and other objects in the robot's vicinity while also contributing to direct low-level control of a Boston Dynamics Atlas robot. Extensive experimental demonstrations illustrate how the approach can enable the humanoid to walk over uneven terrain without stopping (for tens of minutes), which would otherwise not be possible. We characterize the performance of the estimator for each sensor modality and discuss the computational requirements.

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Section: Methodsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Section: Leg Kinematicsmentioning

confidence: 99%

See 1 more Smart Citation

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Fallon

Antone

Roy

et al. 2014

2014 IEEE-RAS International Conference on Humanoid Robots

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“…For example, Bloesch et al [3] first introduced an EKF-based state estimator for a quadruped which Rotella et al [4] extended for bipedal state estimation.…”

Section: Related Work a Localization Of Humanoid Robotsmentioning

confidence: 99%

Overlap-based ICP tuning for robust localization of a humanoid robot

Nobili

Scona

Caravagna

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-State estimation techniques for humanoid robots are typically based on proprioceptive sensing and accumulate drift over time. This drift can be corrected using exteroceptive sensors such as laser scanners via a scene registration procedure. For this procedure the common assumption of high point cloud overlap is violated when the scenario and the robot's point-of-view are not static and the sensor's field-of-view (FOV) is limited. In this paper we focus on the localization of a robot with limited FOV in a semi-structured environment. We analyze the effect of overlap variations on registration performance and demonstrate that where overlap varies, outlier filtering needs to be tuned accordingly. We define a novel parameter which gives a measure of this overlap. In this context, we propose a strategy for robust non-incremental registration. The pre-filtering module selects planar macro-features from the input clouds, discarding clutter. Outlier filtering is automatically tuned at run-time to allow registration to a common reference in conditions of non-uniform overlap. An extensive experimental demonstration is presented which characterizes the performance of the algorithm using two humanoids: the NASA Valkyrie, in a laboratory environment, and the Boston Dynamics Atlas, during the DARPA Robotics Challenge Finals.

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“…Even if the attitude estimation is a classical problem in mobile wheeled robotics [5], [6] and unmanned aerial vehicles (UAV) [7], [8], [9], several researchers have extended Open Solution for Humanoid Attitude Estimation P. Pierro, C. A. Monje, N. Mansard, P. Souères, C. Balaguer such techniques to legged locomotion [10], [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

et al. 2015

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In this paper an Extended Kalman Filter (EKF) is used in order to estimate the real state of a humanoid robot (HRP-2 robot in our case study) using the combination of the information coming from the encoders (kinematics) and from the Inertial Measurement Unit (IMU). The integration of the kinematic information into the Kalman filtering process allows a good estimation of the attitude and reduces the complexity of the problem to the use of simple kinematic transformations, even considering the existence of accelerations and mechanical flexibilities in the robot. The EKF estimator presented here is an open solution directly applicable to any humanoid robot, which is the main contribution of our approach. Experimental results are given showing the good performance of the method.Key words: Humanoid robot, Attitude estimation, Kalman filtering, Sensor integration, Kinematics simplicity Otvoreno rješenje za estimaciju položaja humanoidnog robota integracijom senzora i proširenim Kalmanovim filtrom. U ovomečlanku koristi se prošireni Kalmanov filtar (EKF) za estimaciju stanja humanoidnog robota (HRP-2 robot) kombiniranjem informacija iz enkodera (kinematika) i inercijalne mjerne jedinice (IMU). Integracijom kinematičkih informacija u proces Kalmanovog filtra ostvaruje se dobra estimacija položaja i smanjenje složenosti problema jednostavnim kinematičkim transformacijama,čak i u slučaju postojanja akceleracija te mehaničkih savijanja robota. EKF predstavljen u ovomečlanku otvoreno je rješenje primjenjivo na bilo kojem humanoidnom robotu, što je i glavni doprinos predloženog pristupa. Ekperimentalni rezultati pokazuju dobro ponašanje predložene metode.

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State Estimation for Legged Robots - Consistent Fusion of Leg Kinematics and

Cited by 163 publications

References 15 publications

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

Overlap-based ICP tuning for robust localization of a humanoid robot

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

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