Robot@Home, a robotic dataset for semantic mapping of home environments

Ruiz-Sarmiento, José-Raúl; Galindo, Cipriano; González-Jiménez, Javier

doi:10.1177/0278364917695640

Cited by 70 publications

(41 citation statements)

References 43 publications

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“…The ground truth of this dataset comes from the model of the room. The Robot@Home dataset [16] consists of the data stream coming from 4 RGB-D cameras and 2 2D Lidar, but there is no pose ground truth. The TUM-RGBD dataset [4], as another indoor dataset, is captured by a synchronized Microsoft Kinect RGB-D camera both handheld and onboard of a ground robot.…”

Section: Datasets Surveymentioning

confidence: 99%

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Advanced mapping robot and high-resolution dataset

Chen

Yang

Zhao

et al. 2020

Robotics and Autonomous Systems

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This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization. Nine high-resolution cameras and two 32-beam 3D Lidars were used along with a professional, static 3D scanner for ground truth map collection. With all the sensors calibrated on the mapping robot, three datasets are collected to evaluate the performance of mapping algorithms within a room and between rooms. Based on these datasets we generate maps and trajectory data, which is then fed into evaluation algorithms. We provide the datasets for download and the mapping and evaluation procedures are made in a very easily reproducible manner for maximum comparability. We have also conducted a survey on available robotics-related datasets and compiled a big table with those datasets and a number of properties of them.

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Section: Datasets Surveymentioning

confidence: 99%

“…In Appendix I we have studied mobile ground mapping robot datasets among all the available datasets, including some recent and named datasets such as Rosario Dataset [26], Robot@Home [16], and Cheliean Underground [27]. One of the most recent datasets is Rosario Dataset [26].…”

Section: Datasets Surveymentioning

confidence: 99%

Advanced mapping robot and high-resolution dataset

Chen

Yang

Zhao

et al. 2020

Robotics and Autonomous Systems

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“…A detailed description of each one is presented next. The fact that the first three of the four chosen datasets belong to the Robot@Home dataset [48] is due to the lack of publicly available databases that contain occupancy grids of real houses, being usually focused on labs and offices instead. .…”

Section: Laser-based Maps Of Real Housesmentioning

confidence: 99%

Automatic Waypoint Generation to Improve Robot Navigation Through Narrow Spaces

Moreno

Monroy

Ruiz-Sarmiento

et al. 2019

Sensors

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In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.

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“…For the indoor experimental evaluation, we used the Giraff mobile robot [23], [24] equipped with an odometer (by integrating wheel encoders for a differential drive configuration), an Hokuyo UTW-30LX 2D laser scanner and an uEye UI-1240SE-M-GL monocular camera. The laser scanner is mounted in parallel to the ground, while the camera is pointing downwards with an incidence angle of about 40°, as depicted in Fig.…”

Section: Indoor Evaluationmentioning

confidence: 99%

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

Zuñiga-Noël

Ruiz-Sarmiento

Gomez-Ojeda

et al. 2019

IEEE Robot. Autom. Lett.

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In order to fuse measurements from multiple sensors mounted on a mobile robot, it is needed to express them in a common reference system through their relative spatial transformations. In this paper, we present a method to estimate the full 6DoF extrinsic calibration parameters of multiple heterogeneous sensors (Lidars, Depth and RGB cameras) suitable for automatic execution on a mobile robot. Our method computes the 2D calibration parameters (x, y, yaw) through a motion-based approach, while for the remaining 3 parameters (z, pitch, roll) it requires the observation of the ground plane for a short period of time. What set this proposal apart from others is that: i) all calibration parameters are initialized in closed form, and ii) the scale ambiguity inherent to motion estimation from a monocular camera is explicitly handled, enabling the combination of these sensors and metric ones (Lidars, stereo rigs, etc.) within the same optimization framework. We provide a formal definition of the problem, as well as of the contributed method, for which a C++ implementation has been made publicly available. The suitability of the method has been assessed in simulation an with real data from indoor and outdoor scenarios. Finally, improvements over state-of-the-art motion-based calibration proposals are shown through experimental evaluation.

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Robot@Home, a robotic dataset for semantic mapping of home environments

Cited by 70 publications

References 43 publications

Advanced mapping robot and high-resolution dataset

Advanced mapping robot and high-resolution dataset

Automatic Waypoint Generation to Improve Robot Navigation Through Narrow Spaces

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

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