An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Sprunk, Christoph; Röwekämper, Jörg; Parent, Gershon; Spinello, Luciano; Tipaldi, Gian Diego; Burgard, Wolfram; Jalobeanu, Mihai

doi:10.1007/978-3-319-23778-7_32

Cited by 26 publications

(18 citation statements)

References 11 publications

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“…The sequence was repeated 12 times, for a total of 3km, and every time a different set of modifications was made to the environment, such as people walking by or blocking the robot, doors being closed, obstacles placed in the path and furniture being disturbed or moved to new locations. For a complete description of the benchmark procedure see [4].…”

Section: A Benchmarking Methodsmentioning

confidence: 99%

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Reliable kinect-based navigation in large indoor environments

Jalobeanu

Shirakyan

Parent

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Practical mapping and navigation solutions for large indoor environments continue to rely on relatively expensive range scanners, because of their accuracy, range and field of view. Microsoft Kinect on the other hand is inexpensive, is easy to use and has high resolution, but suffers from high noise, shorter range and a limiting field of view. We present a mapping and navigation system that uses the Microsoft Kinect sensor as the sole source of range data and achieves performance comparable to state-of-theart LIDAR-based systems. We show how we circumvent the main limitations of Kinect to generate usable 2D maps of relatively large spaces and to enable robust navigation in changing and dynamic environments. We use the Benchmark for Robotic Indoor Navigation (BRIN) to quantify and validate the performance of our system.

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Section: A Benchmarking Methodsmentioning

confidence: 99%

“…We include experimental results based on the Benchmark for Robotic Indoor Navigation (BRIN) [4] that quantify the performance of our approach relative to state-of-the-art systems.…”

Section: Introductionmentioning

confidence: 99%

Reliable kinect-based navigation in large indoor environments

Jalobeanu

Shirakyan

Parent

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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“…For this reason, previous work focused on establishing benchmarks in specific sub-fields of robotics research, e.g. manipulation [5], motion planning [6], [7], navigation [8], [9], service…”

Section: Background and Related Workmentioning

confidence: 99%

The HRC Model Set for Human-Robot Collaboration Research

Zeylikman

Widder

Roncone

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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In this paper, we present a model set for designing human-robot collaboration (HRC) experiments. It targets a common scenario in HRC, which is the collaborative assembly of furniture, and it consists of a combination of standard components and custom designs. With this work, we aim at reducing the amount of work required to set up and reproduce HRC experiments, and we provide a unified framework to facilitate the comparison and integration of contributions to the field. The model set is designed to be modular, extendable, and easy to distribute. Importantly, it covers the majority of relevant research in HRC, and it allows tuning of a number of experimental variables that are particularly valuable to the field. Additionally, we provide a set of software libraries for perception, control and interaction, with the goal of encouraging other researchers to proactively contribute to our work.

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“…On the other hand, distance traveled alone is a poor indicator because it completely ignores the amount of area (mobile base plus onboard manipulator) covered by the mobile manipulator. In 2014, Sprunk et al [79] defined an experimental protocol to evaluate the whole navigation system, deployed in a real environment. To ensure repeatability and reproducibility of experiments, their benchmark protocol provided detailed definitions and controls for the environment dynamics (i.e., variations in different environments).…”

Section: Performance Metricsmentioning

confidence: 99%

Survey of Research for Performance Measurement of Mobile Manipulators

Bostelman¹,

Hong²,

Marvel³

2016

J. RES. NATL. INST. STAN.

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This survey provides the basis for developing research in the area of mobile manipulator performance measurement, an area that has relatively few research articles when compared to other mobile manipulator research areas. The survey provides a literature review of mobile manipulator research with examples of experimental applications. The survey also provides an extensive list of planning and control references as this has been the major research focus for mobile manipulators which factors into performance measurement of the system. The survey then reviews performance metrics considered for mobile robots, robot arms, and mobile manipulators and the systems that measure their performance, including machine tool measurement systems through dynamic motion tracking systems. Lastly, the survey includes a section on research that has occurred for performance measurement of robots, mobile robots, and mobile manipulators beginning with calibration, standards, and mobile manipulator artifacts that are being considered for evaluation of mobile manipulator performance.

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An Experimental Protocol for Benchmarking Robotic Indoor Navigation

Cited by 26 publications

References 11 publications

Reliable kinect-based navigation in large indoor environments

Reliable kinect-based navigation in large indoor environments

The HRC Model Set for Human-Robot Collaboration Research

Survey of Research for Performance Measurement of Mobile Manipulators

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