Assessment of Laser Range Finders in risky environments

Pascoal, Jorge F.; Marques, Lino; Almeida, Anı́bal T. de

doi:10.1109/iros.2008.4650961

Cited by 31 publications

(35 citation statements)

References 9 publications

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“…Smoke obstructs perception in the visible spectrum; this is the case for the human eye as well as for sensors such as cameras but also for laser range finders (LRF) as our experiments confirmed [43].…”

Section: Navigating In Smokesupporting

confidence: 71%

A Robot Swarm Assisting a Human Fire-Fighter

et al. 2011

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Emergencies in industrial warehouses are a major concern for fire fighters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus the robot swarm is able to provide guidance information to the humans. Together with the fire fighters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings.keywords: Swarm robotics, search and rescue, human robot (swarm) interface, mobile ad-hoc networks.

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Section: Navigating In Smokesupporting

confidence: 71%

A Robot Swarm Assisting a Human Fire-Fighter

et al. 2011

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“…Pascoal et al [20] carried out a set of tests in order to analyze the behavior of distinct LRFs within low visibility scenarios. Smoke was progressively injected in the scenario using a smoke machine and spread by means of a ventilator.…”

Section: Related Workmentioning

confidence: 99%

A Sensor Fusion Layer to Cope with Reduced Visibility in SLAM

Santos

Couceiro

Portugal

et al. 2015

J Intell Robot Syst

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Mapping and navigating with mobile robots in scenarios with reduced visibility, e.g. due to smoke, dust, or fog, is still a big challenge nowadays. In spite of the tremendous advance on Simultaneous Localization and Mapping (SLAM) techniques for the past decade, most of current algorithms fail in those environments because they usually rely on optical sensors providing dense range data, e.g. laser range finders, stereo vision, LIDARs, RGB-D, etc., whose measurement process is highly disturbed by particles of smoke, dust, or steam. This article addresses the problem of performing SLAM under reduced visibility conditions by proposing a sensor fusion layer which takes advantage from complementary characteristics between a laser range finder (LRF) and an array of sonars. This sensor fusion layer is ultimately used with a state-of-the-art SLAM technique to be resilient in scenarios where visibility cannot be assumed at all times. Special attention is given to mapping using commercial off-the-shelf (COTS) sensors, namely arrays of sonars which, being usually available in robotic platforms, raise technical issues that were investigated in the course of this work. Two sensor fusion methods, a heuristic method and a fuzzy logic-based method, are presented and discussed, corresponding to different stages of the research work conducted. The experimental validation of both methods with two different mobile robot platforms in smoky indoor scenarios showed that they provide a robust solution, using only COTS sensors, for adequately coping with reduced visibility in the SLAM process, thus decreasing significantly its impact in the mapping and localization results obtained.

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“…• Interaction of the incident wave with the target surface these and other sources of errors in the ranging measurement of the laser range sensors have been the motivation to investigate additional characteristics of such sensors (Pascoal et al, 2008), (Ye & Borenstein, 2002) and also to find a model that can describe the likelihood that a given measurement obtained from these devices can be relied as true and not faulty measurements. This analysis is not within the scope of this chapter and shall not be considered.…”

Section: Digital Elevation Mapmentioning

confidence: 99%