DRC Team NimbRo Rescue: Perception and Control for Centaur-Like Mobile Manipulation Robot Momaro

Schwarz, Max; Beul, Marius; Droeschel, David; Klamt, Tobias; Lenz, Christian; Pavlichenko, Dmytro; Rodehutskors, Tobias; Schreiber, Michael; Araslanov, Nikita; Ivanov, Ivan; Razlaw, Jan; Schüller, Sebastian; Schwarz, David; Topalidou-Kyniazopoulou, Angeliki; Behnke, Sven

doi:10.1007/978-3-319-74666-1_5

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…They proposed a novel approach for rendering a PCVE from robot sensor data. A number of authors have extended this idea using different approaches to render PVCEs ( Schwarz et al, 2017 ; Lesniak and Tucker, 2018 ; Valenzuela-Urrutia et al, 2019 ), Voxel Virtual Environments (VVEs) ( Mossel and Kroeter, 2017 ; Zhou and Tuzel, 2018 ; Stotko et al, 2019 ; Li et al, 2022 ), and VEs composed of precomposed objects based on automated recognition of objects from the point cloud data ( Zhou et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, in addition to the factors affecting the density with which point clouds can be rendered listed above, point clouds may need to be coloured to display sensor information present at each point (rather than visible colour) such as temperature or radiation. A common use of artificial point colouring is to display point elevation ( Bergé et al, 2016 ; Schwarz et al, 2017 ). Figure 1 shows a point cloud coloured in this way.…”

Section: Introductionmentioning

confidence: 99%

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Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data

Bremner

Giuliani

2022

Front. Robot. AI

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This paper makes a contribution to research on digital twins that are generated from robot sensor data. We present the results of an online user study in which 240 participants were tasked to identify real-world objects from robot point cloud data. In the study we manipulated the render style (point clouds vs voxels), render resolution (i.e., density of point clouds and granularity of voxel grids), colour (monochrome vs coloured points/voxels), and motion (no motion vs rotational motion) of the shown objects to measure the impact of these attributes on object recognition performance. A statistical analysis of the study results suggests that there is a three-way interaction between our independent variables. Further analysis suggests: 1) objects are easier to recognise when rendered as point clouds than when rendered as voxels, particularly lower resolution voxels; 2) the effect of colour and motion is affected by how objects are rendered, e.g., utility of colour decreases with resolution for point clouds; 3) an increased resolution of point clouds only leads to an increased object recognition if points are coloured and static; 4) high resolution voxels outperform medium and low resolution voxels in all conditions, but there is little difference between medium and low resolution voxels; 5) motion is unable to improve the performance of voxels at low and medium resolutions, but is able to improve performance for medium and low resolution point clouds. Our results have implications for the design of robot sensor suites and data gathering and transmission protocols when creating digital twins from robot gathered point cloud data.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data

Bremner

Giuliani

2022

Front. Robot. AI

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“…Robot Platform: In Schwarz et al [36], the rescue robot Momaro is described, which is equipped with interfaces for immersive teleoperation using an HMD device and 6D track- WiFi Fig. 2.…”

Section: Related Workmentioning

confidence: 99%

A VR System for Immersive Teleoperation and Live Exploration with a Mobile Robot

Stotko

Krumpen

Schwarz

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Applications like disaster management and industrial inspection often require experts to enter contaminated places. To circumvent the need for physical presence, it is desirable to generate a fully immersive individual live teleoperation experience. However, standard video-based approaches suffer from a limited degree of immersion and situation awareness due to the restriction to the camera view, which impacts the navigation. In this paper, we present a novel VR-based practical system for immersive robot teleoperation and scene exploration. While being operated through the scene, a robot captures RGB-D data that is streamed to a SLAM-based live multiclient telepresence system. Here, a global 3D model of the already captured scene parts is reconstructed and streamed to the individual remote user clients where the rendering for e.g. head-mounted display devices (HMDs) is performed. We introduce a novel lightweight robot client component which transmits robot-specific data and enables a quick integration into existing robotic systems. This way, in contrast to firstperson exploration systems, the operators can explore and navigate in the remote site completely independent of the current position and view of the capturing robot, complementing traditional input devices for teleoperation. We provide a proofof-concept implementation and demonstrate the capabilities as well as the performance of our system regarding interactive object measurements and bandwidth-efficient data streaming and visualization. Furthermore, we show its benefits over purely video-based teleoperation in a user study revealing a higher degree of situation awareness and a more precise navigation in challenging environments.

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“…With the development of the age of intelligence, the application scenarios and task requirements of intelligent mobile robots are becoming more and more complex [1,2]. A single robot has difficulty in meeting the needs of humans for highly automated robots.…”

Section: Introductionmentioning

confidence: 99%

Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion

Guan

Liu

et al. 2020

Mathematical Problems in Engineering

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Collaborative navigation is the key technology for multimobile robot system. In order to improve the performance of collaborative navigation system, the collaborative navigation algorithms based on odometer/vision multisource information fusion are presented in this paper. Firstly, the multisource information fusion collaborative navigation system model is established, including mobile robot model, odometry measurement model, lidar relative measurement model, UWB relative measurement model, and the SLAM model based on lidar measurement. Secondly, the frameworks of centralized and decentralized collaborative navigation based on odometer/vision fusion are given, and the SLAM algorithms based on vision are presented. Then, the centralized and decentralized odometer/vision collaborative navigation algorithms are derived, including the time update, single node measurement update, relative measurement update between nodes, and covariance cross filtering algorithm. Finally, different simulation experiments are designed to verify the effectiveness of the algorithms. Two kinds of multirobot collaborative navigation experimental scenes, which are relative measurement aided odometer and odometer/SLAM fusion, are designed, respectively. The advantages and disadvantages of centralized versus decentralized collaborative navigation algorithms in different experimental scenes are analyzed.

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DRC Team NimbRo Rescue: Perception and Control for Centaur-Like Mobile Manipulation Robot Momaro

Cited by 12 publications

References 34 publications

Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data

Impact of resolution, colour, and motion on object identification in digital twins from robot sensor data

A VR System for Immersive Teleoperation and Live Exploration with a Mobile Robot

Multirobot Collaborative Navigation Algorithms Based on Odometer/Vision Information Fusion

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