Closed-loop MPC with Dense Visual SLAM - Stability through Reactive Stepping

Tanguy, Arnaud; Simone, Daniele De; Comport, Andrew I.; Oriolo, Giuseppe; Kheddar, Abderrahmane

doi:10.1109/icra.2019.8794006

Cited by 17 publications

(15 citation statements)

References 23 publications

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“…As future work, this benchmark will be extended to real indoor industrial and hospital setups that are confined environments, considering locomotion while manipulation by a humanoid robot [38]. Thus, our methodology will be applied in these environments to assess the results while the camera is embedded on a real robot.…”

Section: Discussionmentioning

confidence: 99%

Benchmarking Cameras for Open VSLAM Indoors

Chappellet

Caron²,

Kanehiro

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

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In this paper we benchmark different types of cameras and evaluate their performance in terms of reliable localization reliability and precision in Visual Simultaneous Localization and Mapping (vSLAM). Such benchmarking is merely found for visual odometry, but never for vSLAM. Existing studies usually compare several algorithms for a given camera.The evaluation methodology we propose is applied to the recent OpenVSLAM framework. The latter is versatile enough to natively deal with perspective, fisheye, 360 cameras in a monocular or stereoscopic setup, an in RGB or RGB-D modalities. Results in various sequences containing light variation and scenery modifications in the scene assess quantitatively the maximum localization rate for 360 vision. In the contrary, RGB-D vision shows the lowest localization rate, but highest precision when localization is possible. Stereo-fisheye trades-off with localization rates and precision between 360 vision and RGB-D vision.The dataset with ground truth will be made available in open access to allow evaluating other/future vSLAM algorithms with respect to these camera types.

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Section: Discussionmentioning

confidence: 99%

Benchmarking Cameras for Open VSLAM Indoors

Chappellet

Caron²,

Kanehiro

et al. 2021

2020 25th International Conference on Pattern Recognition (ICPR)

Self Cite

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“…This paper focuses on the practical implementation of such subsystem and its implementation on a research humanoid robot platform, here the TALOS robot from the company PAL-Robotics. Similar work has been realized using a Dense SLAM system [11], with a HRP-4 humanoid robot and an off-board computer equipped with a GPU. In this work we focus on the localization performance using an onboard computation.…”

Section: A State Of the Artmentioning

confidence: 99%

ICP Localization and Walking Experiments on a TALOS Humanoid Robot

Lasguignes

Maroger

Fallon

et al. 2021

2021 20th International Conference on Advanced Robotics (ICAR)

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This system paper describes the integration and the evaluation of an ICP-based localization system on the TALOS humanoid robot. The new generation of flash LiDAR systems, here an Ouster OS1-64, have made it possible to obtain 3D clouds at 10 Hz. Coupled with an Intel RealSense T265 providing visual-inertial odometry it is possible to localize the robot and use this information to generate foot steps in real time to reach specific points. The approach is validated with a Qualisys motion capture system. It is also used to generate real-time walking motion on the TALOS humanoid robot. This paper is an integration paper showing that it is now feasible to accurately guide a humanoid robot in an environment in real time using a LiDAR system.

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“…Model predictive control (MPC) [17], [18] is widely applied in robotics as a traditional approach for control, which has been testified to be effective in many robotic control tasks, including quadrotor control [19], dynamic legged robot control [20], robotic arm manipulations [21], SLAM of locomotory robots [22], autonomous aerial vehicles [23], etc. The key insight of MPC is to continuously solve an optimisation problem online over a short horizon by making use of a system dynamic model to predict future states, including the controllable robot and its environment.…”

Section: Related Workmentioning

confidence: 99%

DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos

Yuan¹,

Wu²,

Zhao³

et al. 2021

Preprint

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Learning an accurate model of the environment is essential for model-based control tasks. Existing methods in robotic visuomotor control usually learn from data with heavily labelled actions, object entities or locations, which can be demanding in many cases. To cope with this limitation, we propose a method that trains a forward model from video data only, via disentangling the motion of controllable agent to model the transition dynamics. An object extractor and an interaction learner are trained in an end-to-end manner without supervision. The agent's motions are explicitly represented using spatial transformation matrices containing physical meanings. In the experiments, our method achieves superior performance on learning an accurate forward model in a Grid World environment, as well as a more realistic robot control environment in simulation. With the accurate learned forward models, we further demonstrate their usage in model predictive control as an effective approach for robotic manipulations.

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Closed-loop MPC with Dense Visual SLAM - Stability through Reactive Stepping

Cited by 17 publications

References 23 publications

Benchmarking Cameras for Open VSLAM Indoors

Benchmarking Cameras for Open VSLAM Indoors

ICP Localization and Walking Experiments on a TALOS Humanoid Robot

DMotion: Robotic Visuomotor Control with Unsupervised Forward Model Learned from Videos

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