Application of Millimetre Wave Radar Sensor to Environment Mapping in Surface Mining

Widzyk-Capehart, E.; Brooker, Graham; Scheding, Steve; Hennessy, R.; Maclean, A.; Lobsey, C.

doi:10.1109/icarcv.2006.345324

Cited by 15 publications

(14 citation statements)

References 3 publications

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“…Typically, millimeter-wave radar in the mining industry has been limited to slope stability monitoring (Macfarlane & Robertson, 2004;Reeves, Stickley, Noon, & Longstaff, 2000) (and systems by GroundProbe and Reutech) or imaging large underground cavities [stopes, ore passes (Brooker, Hennesy, Scheding, & Bishop, 2005;Noon et al, 2002)], although there is a growing focus on its application to environmental mapping, volume estimation, and machine component tracking (e.g., bucket localization). In general, this mining focus has been spearheaded by work done at the Australian Centre for Field Robotics and CRCMining (Brooker et al, 2005;Brooker, Hennesy, Lobsey, Bishop, & WidzykCapehart, 2007;Scheding, Brooker, Hennesy, Bishop, & Maclean, 2002;Widzyk-Caperhart, Brooker, Hennesy, Lobsey, & Scheding, 2006). Scanning laser range finders operating in the (near) infrared have found more widespread application in the mining environment, arguably due to the lower sensor costs and maturity of the technology.…”

Section: Introductionmentioning

confidence: 99%

Performance of laser and radar ranging devices in adverse environmental conditions

Ryde

Hillier

2009

Journal of Field Robotics

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A comparative evaluation of millimeter-wave radar and two-dimensional scanning lasers in dust and rain conditions for sensor applications in field robotics is presented. A robust and reliable method for measuring the level of suspended dust (or other obscurant media) by determining the transmission coefficient is developed and used for quantitative assessment of sensor performance. The criteria of target acquisition reliability, precision, and accuracy under varying environmental conditions are assessed via sensor operation in a controlled environment. This environment generated dust and rain of varying densities. Sensor performance is also assessed for the potential effect on digital terrain mapping and haul truck localization due to sensor-specific behaviors in these conditions (e.g., false targets, increased noise). Trials on a research electric face shovel are conducted to test observed behaviors. It is concluded that laser scanners are suitable for environments with transmissions exceeding 92%-93%/m for targets closer than 25 m. The radar remained relatively unaffected by the generated conditions of rain (50-70 mm/h) and dust (10-m visibility); however, its accuracy (0.1 m with a corner reflector and 0.3 m on a haul truck), free-space clutter, and scan rate were insufficient for locating unmodified haul trucks for this application. C 2009 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

Performance of laser and radar ranging devices in adverse environmental conditions

Ryde

Hillier

2009

Journal of Field Robotics

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“…15. From a qualitative 9 Note that the 360 scans, can be best visualized in color. point-of-view, it can be seen that artefacts such as the pillars, cars and concrete corner section correspond well between the predicted and actual scans.…”

Section: B Full 360 Scan Spectra Predictionmentioning

confidence: 99%

“…This coefficient provides a measure of the strength of the linear relationship between the predicted spectra , with spectral values , and the observed range spectra , with spectral values , and will provide an indication of how accurate the predicted observations (spectra) are. This coefficient is given by (9) where each summation is taken from to with being the number of bins in each power versus range spectra at a given scanning bearing angle (in this case ). A value of shows the two spectra are fully correlated and shows that the two spectra are uncorrelated.…”

Section: B Range Noise Estimationmentioning

confidence: 99%

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Predicting Millimeter Wave Radar Spectra for Autonomous Navigation

Jose¹,

Adams

Mullane

et al. 2010

IEEE Sensors J.

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Millimeter Wave (MMW) radars are currently used as range measuring devices in applications such as automotive driving aids (Langer and Jochem, 1996), (Rohling and Mende, 1996), the mapping of mines (Brooker et al., 2005) and autonomous field robotics (Brooker, 2001), (Langer, 1996). This recent interest is largely due to the advantages MMW radars offer over other range measuring sensors, as their performance is less affected by dust, fog, rain or snow and ambient lighting conditions. MMW radars can provide received signal strength values, at all discrete range intervals, within the working range of the radar (Clark and Durrant-Whyte, 1997), (Scheding et al., 2002). The received power versus range spectra hence contain useful range to target information, but are also corrupted by noise. User defined stochastic algorithms can then be implemented, which exploit this rich data to improve object detection and mapping performance. This is in contrast to many other range measuring devices which typically internally threshold received signals, to provide single hard decisions only, on the estimated range to objects (Mullane, 2007). This paper addresses the issues of predicting the power-range spectra from MMW radars which use the Frequency Modulated Continuous Wave (FMCW) range estimation technique. This is important for two reasons. First, in automotive and autonomous robotic applications, such sensors are used in conjunction with vehicle navigation and map state estimation filters. This is so that (typically uncertain) vehicle motion knowledge can be optimally fused with the noisy sensor information, to infer estimates of the state of interest (typically, the vehicle's pose (position and orientation) and/or information of the surrounding object locations). Hence, it is essential that predicted power versus range spectra can be computed, to apply a Bayesian recursive estimation framework, based on previous measurements, and uncertain vehicle motion information. Second, it is extremely useful to be able to simulate MMW radar data, given certain environmental configurations. This aids the development of reliable object detection algorithms, based on theoretical sensor and noise models, which can then be applied more effectively to real MMW radar data.Index Terms-Millimeter wave RADAR, power range spectra, autonomous robotics.

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“…Other work realized at the ACFR is related on the development of both short-range high-speed imaging (single axis scan), and longer range lower frame rate using two axis mirror scanner in order to produce 3D images. Initially designed for the mining domain [14], these systems are interesting for terrain imaging in robotics applications. A summary of the use of three types of millimeter-wave radar for navigation and visualization in unstructured environments can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

Mapping of the environment with a high resolution ground-based radar imager

Rouveure¹,

Monod²,

Faure³

2008

MELECON 2008 - The 14th IEEE Mediterranean Electrotechnical Conference

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The aim of this paper is to present the potential of microwave radar as a high resolution ground-based imager, in order to build radar maps in environmental applications. A new radar sensor named K2Pi, based on the principle of Frequency-Modulated Continuous Wave (FM-CW) is described. In order to build the radar maps, the R-SLAM algorithm has been developed. It is based on Simultaneous Localization And Mapping (SLAM) principles. The global radar map is constructed through a data merging process, using scan matching of radar image sequences. First results obtained in different environments are presented, which show the ability of the microwave radar to deal with unstructured and extended environments, and to build consistent maps.Index Terms-Environmental mapping, microwave radar, simultaneous localization and mapping.

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Application of Millimetre Wave Radar Sensor to Environment Mapping in Surface Mining

Cited by 15 publications

References 3 publications

Performance of laser and radar ranging devices in adverse environmental conditions

Performance of laser and radar ranging devices in adverse environmental conditions

Predicting Millimeter Wave Radar Spectra for Autonomous Navigation

Mapping of the environment with a high resolution ground-based radar imager

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