Trabajo presentado en el 19th International Conference on Humanoid Robots (Humanoids), celebrado en Toronto (Canadá) del 15 al 17 de octubre de 2019 ; Current locomotion algorithms in structured (in-door) 3D environments require an accurate localization. The several and diverse sensors typically embedded on legged robots (IMU, coders, vision and/or LIDARS) should make it possible if properly fused. Yet this is a difficult task due to the hetero-geneity of these sensors and the real-time requirement of the control. While previous works were using staggered approaches (odometry at high frequency, sparsely corrected from vision and LIDAR localization), the recent progress in optimal estimation, in particular in visual-inertial localization, is paving the way to a holistic fusion. This paper is a contribution in this direction. We propose to quantify how a visual-inertial navigation system can accurately localize a humanoid robot in a 3D indoor environment tagged with fiducial markers. We introduce a theoretical contribution strengthening the formulation of Forster's IMU pre-integration, a practical contribution to avoid possible ambiguity raised by pose estimation of fiducial markers, and an experimental contribution on a humanoid dataset with ground truth. Our system is able to localize the robot with less than 2 cm errors once the environment is properly mapped. This would naturally extend to additional measurements corresponding to leg odometry (kinematic factors) thanks to the genericity of the proposed pre-integration algebra. ; This work has been supported by the MEMMO European Union project within the H2020 Program under Grant Agreement No. 780684, the Spanish Ministry of Science, Innovation, and Universities project EB-SLAM (DPI2017-89564-P), by the EU H2020 project GAUSS (DPI2017-89564-P) and by the Spanish State Research Agency through the Maria de Maeztu Seal of Excellence to IRI MDM-2016-0656.
Absolute humanoid localization and mapping based on IMU Lie group and fiducial markers
2019-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
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