Obstacle detection and navigation planning for autonomous micro aerial vehicles

Nieuwenhuisen, Matthias; Droeschel, David; Beul, Marius; Behnke, Sven

doi:10.1109/icuas.2014.6842355

Cited by 35 publications

(21 citation statements)

References 37 publications

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“…This capability is of paramount importance in classical mobile robots, however, this is transformed into a huge necessity in the special case of autonomous aerial vehicles in order to implement algorithms that generate collision free paths, while significantly increasing the UAV's autonomy, especially in missions where there is no line of sight. Figure 7 presents visualized obstacle free paths a) [50],b) [91] c) [92], d) [93]. In this figure different obstacle detection and avoidance approaches are presented, where a), b) and c) depict identified obstacles in 3D and d) in 2D.…”

Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

274

116

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During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges C. Kanellakis ( ) · G. Nikolakopoulos Luleå University of Technology, Luleå, Sweden e-mail: christoforos.kanellakis@ltu.se G. Nikolakopoulos e-mail: geonik@ltu.se addressed and future trends in autonomous agent development will be also provided.

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Section: Obstacle Detectionmentioning

confidence: 99%

Survey on Computer Vision for UAVs: Current Developments and Trends

Kanellakis

Nikolakopoulos

2017

J Intell Robot Syst

274

116

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“…Among MAVs, Grzonka et al [5] considered a quadrotor equipped with an on-board laser scanner, and scanned the environment, adapting the trajectory as new objects were spotted. Similarly, Nieuwenhuisen et al [6] also used a 3D laser scanner on an autonomous quadrotor to build and update an obstacle map and replan collision-free trajectories. Similar approaches based on different sensors, such as cameras or depth sensors, were proposed in [22].…”

Section: Related Workmentioning

confidence: 99%

“…Similar approaches based on different sensors, such as cameras or depth sensors, were proposed in [22]. However, the previous approaches [5,6,22] rely on configurations that include other sensors (e.g, IMU, Laser Scanner) in addition to cameras. Furthermore, planning is performed without considering the visual perception and, in particular, the photometric information.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the impressive results achieved in visual SLAM applications [1,2], most of vision-controlled MAVs navigate towards a goal location using a predefined set of viewpoints or by remote control, without responding to environmental conditions [3,4]. Recently, several works have tackled the problem of autonomously planning an optimal trajectory towards a goal location [5,6], and others have extended this to uncertainty-aware planning that tries to provide high localization accuracy [7,8]. However, these approaches discard the photometric information (i.e, texture) of the scene and plan the trajectory in advance, which requires prior knowledge of the full 3D map of the environment.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Photometric Information for Planning Under Uncertainty

Delmerico

Werlberger

Valigi

et al. 2017

Springer Proceedings in Advanced Robotics

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Vision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene's visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no pre-computed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot.Abstract Vision-based localization systems rely on highly-textured areas for achieving an accurate pose estimation. However, most previous path planning strategies propose to select trajectories with minimum pose uncertainty by leveraging only the geometric structure of the scene, neglecting the photometric information (i.e, texture). Our planner exploits the scene's visual appearance (i.e, the photometric information) in combination with its 3D geometry. Furthermore, we assume that we have no prior knowledge about the environment given, meaning that there is no precomputed map or 3D geometry available. We introduce a novel approach to update the optimal plan on-the-fly, as new visual information is gathered. We demonstrate our approach with real and simulated Micro Aerial Vehicles (MAVs) that perform perception-aware path planning in real-time during exploration. We show significantly reduced pose uncertainty over trajectories planned without considering the perception of the robot. Supplementary MaterialA video showing the results of our monocular depth estimation approach is available at https://www.youtube.com/watch?v=5UmEw8LDJCI.

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“…• Chapter 7 gives an overall summary of the results obtained in this research, and recommendations for future research. [22]. Laser range finders and ultrasonic sensors such as radar and sonar are capable of high accuracy range measurement to centimeter level, but only provide point measurement at a given position and orientation.…”

Section: Organizationmentioning

confidence: 99%

Obstacle Detection Using Monocular Camera for Low Flying Unmanned Aerial Vehicle

Zhang¹

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This thesis describes the research of an obstacle detection system for a low flying autonomous unmanned aerial vehicle (UAV). The system utilized an extended Kalman filter based simultaneous localization and mapping algorithm which fuses navigation measurements with monocular image sequence to estimate the poses of the UAV and and NSERC (Collaborative Research and Development Grant and Discovery Grant) for their financial support. At last but not the least, I would like to thank my husband Hao Liu, my parents, and my parents in law for their support over the years of my studies. iv

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Obstacle detection and navigation planning for autonomous micro aerial vehicles

Cited by 35 publications

References 37 publications

Survey on Computer Vision for UAVs: Current Developments and Trends

Survey on Computer Vision for UAVs: Current Developments and Trends

Exploiting Photometric Information for Planning Under Uncertainty

Obstacle Detection Using Monocular Camera for Low Flying Unmanned Aerial Vehicle

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