Evaluation of Navigation Sensors in Fire Smoke Environments

Starr, Joseph W.; Lattimer, Brian Y.

doi:10.1007/s10694-013-0356-3

Cited by 66 publications

(43 citation statements)

References 37 publications

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“…Formsma et al (2011) similarly found that smoke appears to LiDAR as a solid object, whereas, Tretyakov and Linder (2011) demonstrated that camera measurements cannot even be made under similar conditions. More recently, a comparative study by Starr and Lattimer (2014) These observed LiDAR behaviours in smoke are similarly explainable using the elastic LiDAR equation described in this paper. Smoke particles are much smaller than dust, ranging 30 nm-800 nm, however mist and fog are comparable in size to mine dust and may range 1 µm -300e µm in diameter (Johnson, 1969).…”

Section: Other Low-visibility Conditionssupporting

confidence: 58%

“…2.4, challenges associated in producing dust-clouds of a defined character, and the difficulty of measuring/quantifying them. Knowledge of sensor performance in dust, or other particulates, from characterisation has been used to aid sensor selection (Starr and Lattimer, 2014;Tretyakov and 19 …”

Section: Characterising Sensor Performance In Dust Is An Inexact Sciencementioning

confidence: 99%

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Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Phillips¹

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This thesis is a study of how light detection and ranging, also known as LiDAR, can be used to meet perception requirements for field robot applications.LiDAR works by having an emitter produce a pulse of light along a known spatial direction. The distance or range to the world along that direction is determined by measuring the time it takes for reflections of the emitted light to be received at a collector. When this is done over multiple spatial directions, a cloud of points is produced that represent a sampling of the world. LiDAR is a mature technology and commercially available sensors are capable of producing point-clouds comprising up to 65,000 points over 360 degree fields of view, that are updated every 50 ms.This thesis is about how an autonomous machine can interpret meaning from these point-clouds in situations where the environment is complex and dynamic, where there is interaction with other agents leading to occlusion and where there is significant dust in the atmosphere.These conditions are systemic to open-cut mining environments, the domain where this thesis draws its motivation. In particular, the thesis uses as its primary example, the perception requirements for automation of a class of large mining excavators known as electric mining shovels. The work is specifically concerned with the problems of using point-clouds from machine mounted sensors to inform and verify knowledge of object position and orientation in the surrounds of these machines, for instance, the trucks being loaded.LiDAR is affected by dust and some see this a fatal flaw for mining robotics. The work starts with an exploration of the behaviour of a commercial LiDAR sensor in the presence of dust and finds that measurements are systematic and predictable. LiDAR sensors are found to exhibit four behaviours and it is shown how these behaviours can be understood from physics-based arguments. Several conclusions emerge, most notably where LiDAR measures dust, it does so to the leading edge of a dust-cloud rather than, say, as a random point within the cloud. This provides powerful insights into how better to use measurements from LiDAR when dust is present, and in particular suggests that dust can be treated like other forms of occlusion.A problem that must be solved to use LiDAR for machine perception is to establish from where each sensor views the world. This is sometimes called sensor registration and it amounts to finding where a sensor is positioned and how it is orientated. This is typically done by placing markers at known locations and computing the sensor registration from where they appear in the sensor data. For mining robot applications, the use of standard marker or artificial-feature-based approaches is infeasible. The thesis develops a method for sensor registration that overcomes this concern by utilizing the geometric structure of the terrain surrounding the autonomous vehicle platform. The method determines the i information content of registration parameters in measurements of the terrain, and update...

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Section: Other Low-visibility Conditionssupporting

confidence: 58%

Section: Characterising Sensor Performance In Dust Is An Inexact Sciencementioning

confidence: 99%

Determining and verifying object pose from LiDAR measurements to support the perception needs of an autonomous excavator

Phillips¹

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“…Short wavelength devices, such as color cameras, infrared cameras and short wavelength thermal cameras, are not available in smoke environments [7]. Although long wavelength thermal cameras work well in smoke environments [7], it is required for fire localization to distinguish fire and other hot objects from thermal images. In a fire scene, there exist several hot objects, for example, fire, smoke, and objects heated by fire.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Active Sounds for Fire Localization

Iwatani

Kasai

Torikai

2017

SICE Journal of Control, Measurement, and System Integration

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: Localization of fire in smoke environments enables fire-fighters to perform fire-fighting operations safely in a short time, and it is also required to develop autonomous fire-fighting robots. This paper provides a sound property available for fire localization by investigating sound transmission through flame-air interfaces, sound transmission across a gas flow whose gas density is different from the air, and sound transmission in smoke. Sound analysis is performed for several sound frequencies, heat release rates and concentrations of smoke. It is shown that sound through flame fluctuates, and the fluctuation range does not depend on the sound frequency, the heat release rate, or the combustion process. Sound through flame can be distinguished from sound through gas-flow or smoke. The sound property shown in this paper informs the existence of flame in the sound path.

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“…1, is being developed to locate and suppress fires inside ships and structures. Through the SAFFiR program, further advancements of artificial intelligent algorithms/ perception systems [7][8][9][10][11] and unmanned fire suppression systems [12] have been developed to enhance autonomous firefighting robots.…”

Section: Introductionmentioning

confidence: 99%

“…[18,20,24,25,29,30] are not applicable to firefighting robots due to the high false positive rate from colors or reflection illuminations similar to that with fire [23]. Due to the fact that RGB cameras may operate in the visible to short wavelength infrared (IR) (less than 1 μm), they are not usable in smoke-filled environments where the visibility has sufficiently decreased [10,23]. In addition, because the performance of the cameras depends on light, these methods cannot provide proper information under local or global darkness, e.g.…”

Section: Introductionmentioning

confidence: 99%