A Robotized Dumper for Debris Removal in Tunnels Under Construction

Tardioli, Danilo; Riazuelo, Luis; Seco, Teresa; Espelosín, Jesús; Lalana, Jorge; Villarroel, José Luis; Montano, Luis

doi:10.1007/978-3-319-70833-1_11

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The presence of a soil so extraordinarily irregular, slippery, and dynamic (the robot tends to plunge into the mud) makes it impossible to identify a clear floor frame to which transform the sensor readings to use standard localization or navigation techniques. Similarly, processing LIDAR information in presence of puddles and deep potholes to generate a feasible and safe navigation plan is remarkably difficult, as confirmed by the preliminary experiments we carried out in Tardioli, Riazuelo, et al (). Another finding that we believe deserves deeper research is the use of the RF signal itself as an ad hoc landmark to help in localization in these environments.…”

Section: Future Workmentioning

confidence: 57%

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

Tardioli

Riazuelo

Sicignano

et al. 2019

Journal of Field Robotics

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The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real-Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel-like environments. As will be discussed, this type of environment presents several special characteristics that often make well-established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.

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Section: Future Workmentioning

confidence: 57%

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

Tardioli

Riazuelo

Sicignano

et al. 2019

Journal of Field Robotics

Self Cite

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“…propose an underground mine mapping algorithm that relies on 2D scan matching and a global alignment step. Tardioli et al [15], [16] propose a system architecture for single and multi-robot exploration in underground tunnels. Zlot et al [17] present a lidar-based SLAM method for mapping of a 17 km underground copper and gold mine; to detect loop closures, they use a surfel representation and search for matches against previous trajectory segments.…”

Section: Introductionmentioning

confidence: 99%

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

Ebadi

Chang

Palieri

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

149

102

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Simultaneous Localization and Mapping (SLAM) in large-scale, unknown, and complex subterranean environments is a challenging problem. Sensors must operate in off-nominal conditions; uneven and slippery terrains make wheel odometry inaccurate, while long corridors without salient features make exteroceptive sensing ambiguous and prone to drift; finally, spurious loop closures that are frequent in environments with repetitive appearance, such as tunnels and mines, could result in a significant distortion of the entire map. These challenges are in stark contrast with the need to build highly-accurate 3D maps to support a wide variety of applications, ranging from disaster response to the exploration of underground extraterrestrial worlds. This paper reports on the implementation and testing of a lidar-based multirobot SLAM system developed in the context of the DARPA Subterranean Challenge. We present a system architecture to enhance subterranean operation, including an accurate lidarbased front-end, and a flexible and robust back-end that automatically rejects outlying loop closures. We present an extensive evaluation in large-scale, challenging subterranean environments, including the results obtained in the Tunnel Circuit of the DARPA Subterranean Challenge. Finally, we discuss potential improvements, limitations of the state of the art, and future research directions.

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“…Global localization is achieved by matching observations of the environment with the map features. In a similar work, Tardioli et al [34] propose an underground localization algorithm to enable autonomous navigation of a commercial dumper commonly used for underground construction. Localization is achieved by relying on semantic feature recognition in the tunnels to provide local information sufficient for a successful localization.…”

Section: Introductionmentioning

confidence: 99%

DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

Ebadi¹,

Palieri²,

Wood³

et al. 2021

Preprint

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Enabling fully autonomous robots capable of navigating and exploring largescale, unknown and complex environments has been at the core of robotics research for several decades. A key requirement in autonomous exploration is building accurate and consistent maps of the unknown environment that can be used for reliable navigation. Loop closure detection, the ability to assert that a robot has returned to a previously visited location, is crucial for consistent mapping as it reduces the drift caused by error accumulation in the estimated robot trajectory. Moreover, in multi-robot systems, loop closures enable merging local maps obtained by a team of robots into a consistent global map of the environment. In this paper, we present a degeneracy-aware and drift-resilient loop closing method to improve place recognition and resolve 3D location ambiguities for simultaneous localization and mapping (SLAM) in GPS-denied, large-scale and perceptually-degraded environments. More specifically, we focus on SLAM in subterranean environments (e.g., lava tubes, caves, and mines) that represent examples of complex and ambiguous environments where current methods have inadequate performance. The first contribution of this paper is a degeneracyaware lidar-based SLAM front-end to determine the observability and level of geometric degeneracy in an unknown environment. Using this crucial capability, ambiguous and unobservable areas in an unknown environment are determined and excluded from the search for loop closures to avoid distortions of the entire map as the result of spurious or inaccurate loop closures. The second contribution of this paper is a drift-resilient loop closing pipeline that exploits the salient 2D and 3D features extracted from lidar point cloud data to enable a

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A Robotized Dumper for Debris Removal in Tunnels Under Construction

Cited by 7 publications

References 10 publications

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments

DARE-SLAM: Degeneracy-Aware and Resilient Loop Closing in Perceptually-Degraded Environments

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