Collaborative Mapping of an Earthquake Damaged Building via Ground and Aerial Robots

Michael, Nathan; Shen, Shaojie; Mohta, Kartik; Kumar, Vijay; Nagatani, Keiji; Okada, Yoshito; Kiribayashi, Seiga; Otake, Kazuki; Yoshida, Kazuya; Ohno, Kazunori; Takeuchi, Eijiro; Tadokoro, Satoshı

doi:10.1007/978-3-642-40686-7_3

Cited by 115 publications

(77 citation statements)

References 17 publications

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“…Hence, the system is readily extendable to groups of more than two robots. The approaches of Michael et al [10] and Forster et al [11] are conceptually similar to ours, yet based on geometric registration, which is known to be sensitive to initial guesses. We experimentally demonstrate that our feature-based visual localization and mapping framework enables reliable operation of both aerial and ground robots using a single map -despite the strongly varying viewpoints of the different agents, and without requiring prior information about the relative poses of the robots.…”

Section: Introductionmentioning

confidence: 92%

Collaborative navigation for flying and walking robots

Fankhauser

Bloesch

Krüsi

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Flying and walking robots can use their complementary features in terms of viewpoint and payload capability to the best in a heterogeneous team. To this end, we present our online collaborative navigation framework for unknown and challenging terrain. The method leverages the flying robot's onboard monocular camera to create both a map of visual features for simultaneous localization and mapping and a dense representation of the environment as an elevation map. This shared knowledge from the flying platform enables the walking robot to localize itself against the global map, and plan a global path to the goal by interpreting the elevation map in terms of traversability. While following the planned path, the absolute pose corrections are fused with the legged state estimation and the elevation map is continuously updated with distance measurements from an onboard laser range sensor. This allows the legged robot to safely navigate towards the goal while taking into account any changes in the environment. In this setup, our approach is independent of external localization, relative observations between the robots, and does not require an initial guess about the pose of the robots. The presented methods are fully integrated and we demonstrate their capabilities in an experiment with a hexacopter and a quadrupedal robot.

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Section: Introductionmentioning

confidence: 92%

Collaborative navigation for flying and walking robots

Fankhauser

Bloesch

Krüsi

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In most related works, the aerial robot flies outdoors higher than 20 meters, and can be localized using GPS [7]- [9]. To the best of our knowledge, the work in [1] is the first to demonstrate how a MAV could assist a ground robot in close collaboration in mapping a damaged building indoors. In their configuration, the ground-robot is equipped with a laser rangefinder and the flying robot with both a laser rangefinder and a RGBD sensor.…”

Section: Related Workmentioning

confidence: 99%

“…The ground robot, on the other hand, can carry more payload such as active depth sensors, processors and may be equipped with a manipulator arm. The usefulness of such a heterogeneous robot team in a disaster scenario has recently been demonstrated in [1].…”

Section: Introductionmentioning

confidence: 99%

Air-ground localization and map augmentation using monocular dense reconstruction

Förster

Pizzoli

Scaramuzza

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of air-ground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-the-art approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features. Air-Ground Localization and Map Augmentation Using Monocular Dense ReconstructionChristian Forster, Matia Pizzoli, Davide ScaramuzzaAbstract-We propose a new method for the localization of a Micro Aerial Vehicle (MAV) with respect to a ground robot. We solve the problem of registering the 3D maps computed by the robots using different sensors: a dense 3D reconstruction from the MAV monocular camera is aligned with the map computed from the depth sensor on the ground robot. Once aligned, the dense reconstruction from the MAV is used to augment the map computed by the ground robot, by extending it with the information conveyed by the aerial views. The overall approach is novel, as it builds on recent developments in live dense reconstruction from moving cameras to address the problem of airground localization. The core of our contribution is constituted by a novel algorithm integrating dense reconstructions from monocular views, Monte Carlo localization, and an iterative pose refinement. In spite of the radically different vantage points from which the maps are acquired, the proposed method achieves high accuracy whereas appearance-based, state-of-theart approaches fail. Experimental validation in indoor and outdoor scenarios reported an accuracy in position estimation of 0.08 meters and real time performance. This demonstrates that our new approach effectively overcomes the limitations imposed by the difference in sensors and vantage points that negatively affect previous techniques relying on matching visual features.

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“…The relative range or relative bearing between different mobile robots, observed by binocular cameras, monocular cameras or other kinds of sensors, is exchanged among the mobile robot group [1,7]. In addition, the performance of this kind of navigation scheme, which is called cooperative navigation, is superior to the one of the separate navigation scheme [8,9]. In a cooperative navigation system, the performance of a low accuracy navigation system will be improved significantly by utilizing the navigation information from a high accuracy navigation system [10,11].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

Sun

Diao²,

Zhang³

et al. 2017

Symmetry

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Abstract:The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.

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Collaborative Mapping of an Earthquake Damaged Building via Ground and Aerial Robots

Cited by 115 publications

References 17 publications

Collaborative navigation for flying and walking robots

Collaborative navigation for flying and walking robots

Air-ground localization and map augmentation using monocular dense reconstruction

Cooperative Localization Algorithm for Multiple Mobile Robot System in Indoor Environment Based on Variance Component Estimation

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