Aerial-guided navigation of a ground robot among movable obstacles

Mueggler, Elias; Faessler, Matthias; Fontana, Flavio; Scaramuzza, Davide

doi:10.1109/ssrr.2014.7017662

Cited by 59 publications

(39 citation statements)

References 22 publications

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“…For example, a flying robot can track a ground robot using a QR-code [1], visual markers [2], [3], or edge detection [4]. Other work uses a camera on a ground robot to track LEDs on a flying robot [5], [6].…”

Section: A Related Workmentioning

confidence: 99%

Collaborative localization of aerial and ground robots through elevation maps

Käslin¹,

Fankhauser²,

Stumm³

et al. 2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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Collaboration between aerial and ground robots can benefit from exploiting the complementary capabilities of each system, thereby improving situational awareness and environment interaction. For this purpose, we present a localization method that allows the ground robot to determine and track its position within a map acquired by a flying robot. To maintain invariance with respect to differing sensor choices and viewpoints, the method utilizes elevation maps built independently by each robot's onboard sensors. The elevation maps are then used for global localization: specifically, we find the relative position and orientation of the ground robot using the aerial map as a reference. Our work compares four different similarity measures for computing the congruence of elevation maps (akin to dense, image-based template matching) and evaluates their merit. Furthermore, a particle filter is implemented for each similarity measure to track multiple location hypotheses and to use the robot motion to converge to a unique solution. This allows the ground robot to make use of the extended coverage of the map from the flying robot. The presented method is demonstrated through the collaboration of a quadrotor equipped with a downward-facing monocular camera and a walking robot equipped with a rotating laser range scanner. Abstract-Collaboration between aerial and ground robots can benefit from exploiting the complementary capabilities of each system, thereby improving situational awareness and environment interaction. For this purpose, we present a localization method that allows the ground robot to determine and track its position within a map acquired by a flying robot. To maintain invariance with respect to differing sensor choices and viewpoints, the method utilizes elevation maps built independently by each robot's onboard sensors. The elevation maps are then used for global localization: specifically, we find the relative position and orientation of the ground robot using the aerial map as a reference. Our work compares four different similarity measures for computing the congruence of elevation maps (akin to dense, image-based template matching) and evaluates their merit. Furthermore, a particle filter is implemented for each similarity measure to track multiple location hypotheses and to use the robot motion to converge to a unique solution. This allows the ground robot to make use of the extended coverage of the map from the flying robot. The presented method is demonstrated through the collaboration of a quadrotor equipped with a downward-facing monocular camera and a walking robot equipped with a rotating laser range scanner.

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Section: A Related Workmentioning

confidence: 99%

Collaborative localization of aerial and ground robots through elevation maps

Käslin¹,

Fankhauser²,

Stumm³

et al. 2016

2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

Self Cite

View full text Add to dashboard Cite

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“…This can then serve a useful asset that can provide a wider field of view, and more environmental information to guide the rover. For example Mueggler et al [6] perform aerial navigation of a youBot using an aerial quadcopter to help route plan through a moveable series of obstacles. The example presented in this paper differs as the ground rover itself becomes a landing site for the quadcopter.…”

Section: A Backgroundmentioning

confidence: 99%

“…2) Installing and Operating on the AR.Drone 2.0: In order to facilitate these tasks for future research, the requisite packages for modifying the AR.Drone 2.0 are available from the Author's page on GitHub 6 . This set of packages provides all of the functionality detailed within this section, natively on startup.…”

Section: Scenario Descriptionmentioning

confidence: 99%

Symbiotic relationship between robots — a ROS ARDrone/YouBot library

Aitken

McAree

Veres

2016

2016 UKACC 11th International Conference on Control (CONTROL)

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Abstract-A Symbiotic relationship between robots is theoretically developed. It is characterised by sharing sensory information and tightly coordinating operational logic by taking care of each other's needs during missions. The system is characterised by an intertwined reasoning system while having separate conditioning and execution of plans to achieve subgoals to support each other. The results are illustrated on strong operational inter-dependence of a rover and a drone through shared logical inference. The drone uses the rover as a landing pad and the rover uses the drone to complements its sensor system by information gathering. There is a GitHub library provided in association with the demonstration for generic use of adding cameras and cooperation logic to a AR.Drone 2.0 and a KUKA youBot system. The benefits of symbiotic relationship are quantitatively evaluated on the demonstration example.

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“…Alternatively, several approaches have been proposed for GPS-independent relative localization of aerial and ground robots. A flying robot is tracked using a sensor mounted on a ground robot [7], or vice versa [8,9], while mapping is accomplished by only one of the agents. The two robots are required to stay close to each other at all times, since the tracked agent can only be localized as long as it is in the field-of-view of the other robot.…”

Section: Introductionmentioning

confidence: 99%

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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Aerial-guided navigation of a ground robot among movable obstacles

Cited by 59 publications

References 22 publications

Collaborative localization of aerial and ground robots through elevation maps

Collaborative localization of aerial and ground robots through elevation maps

Symbiotic relationship between robots — a ROS ARDrone/YouBot library

Collaborative navigation for flying and walking robots

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