An evaluation of stereo and laser‐based range sensing for rotorcraft unmanned aerial vehicle obstacle avoidance

Hrabar, Stefan

doi:10.1002/rob.21404

Cited by 24 publications

(11 citation statements)

References 32 publications

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“…This new scanner was presented as a competition to the smaller scanners proposed by Hokuyo. Evaluations for the Hokuyo series was well investigated with the characterization of the PBS-03JN [5], the UBG-04LX-F01 [11], the URG-04LX [7], [2], [12], [13] and, more recently, the UTM-30LX [14], [15], [16]. Comparisons between Sick LMS-200 and Hokuyo URG scanners were also tackled [8], [7].…”

Section: Related Workmentioning

confidence: 99%

Noise characterization of depth sensors for surface inspections

Pomerleau¹,

Breitenmoser²,

Liu³

et al. 2012

2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI)

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International audienceIn the context of environment reconstruction for inspection, it is important to handle sensor noise properly to avoid distorted representations. A short survey of available sensors is realize to help their selection based on the payload capability of a robot. We then propose uncertainty models based on empirical results for three models of laser rangefinders: Hokuyo URG-04LX, UTM-30LX and the Sick LMS-151. The methodology, used to characterize those sensors, targets more specifically different metallic materials which often give distorted images due to reflexion. We also evaluate the impact of sensor noise on surface normal vector reconstruction and conclude with observations about the impact of sunlight and reflexions

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Section: Related Workmentioning

confidence: 99%

Noise characterization of depth sensors for surface inspections

Pomerleau¹,

Breitenmoser²,

Liu³

et al. 2012

2012 2nd International Conference on Applied Robotics for the Power Industry (CARPI)

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“…In Hrabar (), the author combines stereo vision and LIDAR for static obstacle avoidance for unmanned rotorcraft. 3D occupancy maps are generated online using range data from a stereo vision system, a 2D LIDAR system, and both at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…Different types of imaging sensors have been used to address the mapping and obstacle‐detection problems. Stereo imaging systems have the advantage of providing depth images and ranges to obstacles and have been used on rotary‐wing UASs for obstacle detection and mapping (Andert and Adolf, ; Byrne et al, ; Hrabar, ; Theodore et al, ). Monocular vision (single camera) has also been used as the main perception sensor in different projects (Andert et al, ; Montgomery et al, ; Sanfourche et al, ).…”

Section: Related Workmentioning

confidence: 99%

Dependable Low‐altitude Obstacle Avoidance for Robotic Helicopters Operating in Rural Areas

Merz

Kendoul

2013

Journal of Field Robotics

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This paper presents a system enabling robotic helicopters to fly safely without user interaction at low altitude over unknown terrain with static obstacles. The system includes a novel reactive behavior‐based method that guides rotorcraft reliably to specified locations in sparsely occupied environments. System dependability is, among other things, achieved by utilizing proven system components in a component‐based design and incorporating safety margins and safety modes. Obstacle and terrain detection is based on a vertically mounted off‐the‐shelf two‐dimensional LIDAR system. We introduce two flight modes, pirouette descent and waggle cruise, which extend the field of view of the sensor by yawing the aircraft. The two flight modes ensure that all obstacles above a minimum size are detected in the direction of travel. The proposed system is designed for robotic helicopters with velocity and yaw control inputs and a navigation system that provides position, velocity, and attitude information. It is cost effective and can be easily implemented on a variety of helicopters of different sizes. We provide sufficient detail to facilitate the implementation on single‐rotor helicopters with a rotor diameter of approximately 1.8 m. The system was extensively flight‐tested in different real‐world scenarios in Queensland, Australia. The tests included flights beyond visual range without a backup pilot. Experimental results show that it is feasible to perform dependable autonomous flight using simple but effective methods. © 2013 Wiley Periodicals, Inc.

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“…The recent growth and availability of semiautonomous multirotor helicopters, low‐cost Open Source autopilots, and miniaturization of sensors have resulted in the adoption of unmanned aircraft system (UAS) technology in a wide range of science disciplines, especially in earth observation and aerial surveying. In the field of robotics, Cole, Sukkarieh, and Göktoan (), Hrabar (), and Kendoul () showed significant advances in guidance, navigation, control, obstacle avoidance, and information gathering. Some of these advances in unmanned aerial vehicle (UAV) technology have resulted in the adoption of UASs in more applied fields.…”

Section: Introductionmentioning

confidence: 99%

HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System

Lucieer

Malenovský

Veness

et al. 2014

Journal of Field Robotics

176

146

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One of the key advantages of a low-flying unmanned aircraft system (UAS) is its ability to acquire digital images at an ultrahigh spatial resolution of a few centimeters. Remote sensing of quantitative biochemical and biophysical characteristics of small-sized spatially fragmented vegetation canopies requires, however, not only high spatial, but also high spectral (i.e., hyperspectral) resolution. In this paper, we describe the design, development, airborne operations, calibration, processing, and interpretation of image data collected with a new hyperspectral unmanned aircraft system (HyperUAS). HyperUAS is a remotely controlled multirotor prototype carrying onboard a lightweight pushbroom spectroradiometer coupled with a dual frequency GPS and an inertial movement unit. The prototype was built to remotely acquire imaging spectroscopy data of 324 spectral bands (162 bands in a spectrally binned mode) with bandwidths between 4 and 5 nm at an ultrahigh spatial resolution of 2-5 cm. Three field airborne experiments, conducted over agricultural crops and over natural ecosystems of Antarctic mosses, proved operability of the system in standard field conditions, but also in a remote and harsh, low-temperature environment of East Antarctica. Experimental results demonstrate that HyperUAS is capable of delivering georeferenced maps of quantitative biochemical and biophysical variables of vegetation and of actual vegetation health state at an unprecedented spatial resolution of 5 cm. AbstractOne of the key advantages of a low-flying unmanned aircraft system (UAS) is its ability to acquire digital images at ultra-high spatial resolution of a few centimetres. Remote sensing of quantitative biochemical and biophysical characteristics of small-sized spatially fragmented vegetation canopies requires, however, not only high spatial, but also high spectral (i.e. hyperspectral) resolution. In this paper, we describe the design, development, airborne operations, in addition to calibration, processing and interpretation of image data collected with a new hyperspectral unmanned aircraft system (HyperUAS). HyperUAS is a remotely controlled multi-rotor prototype carrying on-board a * Corresponding author lightweight pushbroom spectroradiometer coupled with a dual frequency global positioning system (GPS) and an inertial movement unit (IMU). The prototype was built to remotely acquire imaging spectroscopy data of 324 spectral bands (162 bands in a spectrally binned mode) with band-widths between 4 and 5 nm at an ultra-high spatial resolution of 2 to 5 cm. Three field airborne experiments, conducted over agricultural crops and over natural ecosystems of Antarctic mosses, proved operability of the system in standard field conditions, but also in a remote and harsh, low-temperature environment of East Antarctica. Experimental results demonstrate that HyperUAS is capable of delivering georeferenced maps of quantitative biochemical and biophysical variables of vegetation and of actual vegetation health state at unprecedented spa...

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An evaluation of stereo and laser‐based range sensing for rotorcraft unmanned aerial vehicle obstacle avoidance

Cited by 24 publications

References 32 publications

Noise characterization of depth sensors for surface inspections

Noise characterization of depth sensors for surface inspections

Dependable Low‐altitude Obstacle Avoidance for Robotic Helicopters Operating in Rural Areas

HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System

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