Rapid exploration with multi-rotors: A frontier selection method for high speed flight

Cieslewski, Titus; Kaufmann, Elia; Scaramuzza, Davide

doi:10.1109/iros.2017.8206030

Cited by 187 publications

(182 citation statements)

References 22 publications

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“…For exploration, the basic requirement for robots is to scan unknown space or detect free space as fast as possible. UGV (unmanned ground vehicle) [13] and UAV (unmanned aerial vehicle) [2] both have been employed for such a task with differences primarily in: (1) UGVs are more payload-capable. A ground vehicle can carry heavy, long-range laser scanners which are inapplicable for weight-constrained UAVs; (2) UAVs have superiors mobility and agility.…”

Section: Motivation Problem Statement Related Workmentioning

confidence: 99%

“…For the UGV's planner, we employ harmonic function with two boundary conditions: occupied cells are assigned zero value, and frontier cells take on the negative value of its frontier A lines' length. The path obtained in this method has several desirable features: (1) Being repelled from obstacles, thus the sensor's range is more utilized (2) Being inclined to lead to longer and nearer frontier lines. Fig.…”

Section: Technical Approachmentioning

confidence: 99%

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A Collaborative Aerial-Ground Robotic System for Fast Exploration

Cheng

Gao

Cai

et al. 2020

Springer Proceedings in Advanced Robotics

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Exploration of unknown environments using autonomous robots has been considered as a fundamental problem in robotics applications such as search and rescue [11], industrial inspection and 3D modelling. For exploration, the basic requirement for robots is to scan unknown space or detect free space as fast as possible. UGV (unmanned ground vehicle) [13] and UAV (unmanned aerial vehicle) [2] both have been employed for such a task with differences primarily in: (1) UGVs are more payload-capable. A ground vehicle can carry heavy, long-range laser scanners which are inapplicable for weight-constrained UAVs; (2) UAVs have superiors mobility and agility. UAV can fly above obstacles and cover areas that are inaccessible to UGVs, like obstacle's top surfaces. Consequently, a UGV often enjoys a larger sensor-coverage, yet cluttered and view-blocking environments could hamper its performance; on the other hand, a UAV may deliver inferior exploration efficiency due to its short-range sensor, but enjoys unblocked downward-looking view. Therefore, UGV favors open areas while UAV prefers cluttered places.In this paper, considering the environmental preferences, we propose an autonomous collaborative framework which utilizes their complimentary characteristics to achieve higher efficiency and robustness in exploration applications.For robotics exploration, [13] first proposed the concept of frontier, which is defined as unknown grid-map cells adjacent to free ones and thus represents accessible new information. Harmonic function, the solution to Laplace sEquation, is used to plan path to frontiers [5]. This method generates a scalar field in free-space based on its surrounding boundary conditions (occupied cells and frontier cells) and obtains the path using gradient-descent. For air-ground exploration, [1] uses the UAV as an back-up instead of an independent explorer. It is only deployed when UGV encounters high, invisible areas.[4] is also proposed based on the same spirit that one vehicle helps another, failing to exploit both vehicles' full potential. Compared to these works, the collaborative system proposed in this paper fully utilizes advantages of different vehicles and thus results in a more efficient exploration. We summarize our contribution as: 1. An efficient exploration framework that combines UAV and UGV's advantages. 2. A more efficient computation method of harmonic function for robotic exploration tasks. 3. Integration of the proposed collaborative exploration framework with the state estimation, sensor fusion and trajectory optimization. Extensive field experiments are presented to validate the efficiency and robustness of the proposed method.

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Section: Motivation Problem Statement Related Workmentioning

confidence: 99%

Section: Technical Approachmentioning

confidence: 99%

A Collaborative Aerial-Ground Robotic System for Fast Exploration

Cheng

Gao

Cai

et al. 2020

Springer Proceedings in Advanced Robotics

View full text Add to dashboard Cite

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“…Many researchers are currently focusing on developing navigation solutions for GNSS denied environments. Most of these are vision-based or involve some sort of multi-sensor fusion (visual-inertial navigation being the most popular [4][5][6]). These methods rely on exteroceptive sensors and thus are inherently dependent on the environment (e.g., enough texture has to be present in the scene) and it is difficult to certify or guarantee their performances.…”

Section: Background and Motivationsmentioning

confidence: 99%

Photometric Long-Range Positioning of LED Targets for Cooperative Navigation in UAVs

Jospin

Stoven-Dubois

Cucci

2019

Drones

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Autonomous flight with unmanned aerial vehicles (UAVs) nowadays depends on the availability and reliability of Global Navigation Satellites Systems (GNSS). In cluttered outdoor scenarios, such as narrow gorges, or near tall artificial structures, such as bridges or dams, reduced sky visibility and multipath effects compromise the quality and the trustworthiness of the GNSS position fixes, making autonomous, or even manual, flight difficult and dangerous. To overcome this problem, cooperative navigation has been proposed: a second UAV flies away from any occluding objects and in line of sight from the first and provides the latter with positioning information, removing the need for full and reliable GNSS coverage in the area of interest. In this work we use high-power light-emitting diodes (LEDs) to signalize the second drone and we present a computer vision pipeline that allows to track the second drone in real-time from a distance up to 100 m and to compute its relative position with decimeter accuracy. This is based on an extension to the classical iterative algorithm for the Perspective-n-Points problem in which the photometric error is minimized according to a image formation model. This extension allow to substantially increase the accuracy of point-feature measurements in image space (up to 0.05 pixels), which directly translates into higher positioning accuracy with respect to conventional methods.

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“…In the current state of the art, a variety of contributions have been made to accomplish autonomous exploration. Most autonomous exploration approaches use frontier exploration [12,13] and, information gain theory, e.g., next-best-view(NBV) [14]. Different types of maps can be constructed to project different information such as topological occupancy maps, metric maps, and semantic maps.…”

Section: Introductionmentioning

confidence: 99%

Victim Localization in USAR Scenario Exploiting Multi-Layer Mapping Structure

et al. 2019

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Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance.

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Rapid exploration with multi-rotors: A frontier selection method for high speed flight

Cited by 187 publications

References 22 publications

A Collaborative Aerial-Ground Robotic System for Fast Exploration

A Collaborative Aerial-Ground Robotic System for Fast Exploration

Photometric Long-Range Positioning of LED Targets for Cooperative Navigation in UAVs

Victim Localization in USAR Scenario Exploiting Multi-Layer Mapping Structure

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