Detecting water hazards for autonomous off-road navigation

Matthies, Larry; Bellutta, P.; McHenry, Mike

doi:10.1117/12.496942

Cited by 37 publications

(14 citation statements)

References 5 publications

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“…Figure 1 generalizes the approaches taken by the PerceptOR teams. The researchers have documented the autonomous robotics technology developed ( Ansar et al, 2004;Huertas et al, 2005;Kelly et al, 2004;Kluge and Morgenthaler, 2004;Matthies et al, 2003aMatthies et al, , 2003bMatthies and Rankin, 2003;Ollis and Jochem, 2003;Rankin et al, 2005;Spofford et al, 2003;Stentz et al, , 2003Vandapel et al, 2003Vandapel et al, , 2006, so this article will neither present nor review those contributions.…”

Section: Introductionmentioning

confidence: 96%

The DARPA PerceptOR evaluation experiments

Krotkov¹,

Fish²,

Jackel

et al. 2006

Auton Robot

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This article describes the conduct of six evaluation experiments for the Perception for Off-Road Robotics program. Key distinctions of the testing methodology include conduct of the experiments by a group independent from the developers, unrehearsed experiments that provide little advance knowledge of the test courses, and blind experiments that do not allow the system operators to see the test courses until testing has completed.The article presents quantified, objective performance metrics for the systems evaluated. The basis for evaluation is E. Krotkov ( ) Griffin Technologies, 296 runs traveling 130 km in 110 hr. The results show significant progress over the course of the program, reducing the Emergency-Stops per kilometer by a factor of 22, reducing the uplink data volume per unit distance by a factor of 46 and the downlink data volume per unit distance by a factor of 3.At the end of Phase III, typical performance in desert terrain by the most reliable system achieved travel speed of 66 cm/s covering 90% of the distance in autonomous mode.

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Section: Introductionmentioning

confidence: 96%

The DARPA PerceptOR evaluation experiments

Krotkov¹,

Fish²,

Jackel

et al. 2006

Auton Robot

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“…However, the problem with many AID systems is that the accuracy of detection is reduced by the existence of external outdoor environmental factors. The authors have performed a huge analysis on the different types of these outdoor factors [37] and they concluded that there are main 4 major factors: the presence of static shadows [6] and [31], static glare, snow movement [39] or rain [32][33][34]. In ITSC 2006, we presented a paper that addressed the problem of static shadow detection [38].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm to Compensate for Road Illumination Changes for AID Systems

Cai¹,

Shehata

Badawy

et al. 2007

2007 IEEE Intelligent Transportation Systems Conference

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Many ITS systems suffer from high false alarm rates due to static glare. Static glare appears in video scenes when the road is wet and light originating from static light source, such as road lamp, is reflected on the road. Often an Automatic Incidents Detection (AID) system will detect occurrences of static glare as incidents, causing the generation of false alarms. In this paper, an effective real-time algorithm is proposed to detect static glare. This algorithm generates a static glare map that can be used by an AID system to avoid static glare false alarms.

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“…Under the U.S. Army Research Laboratory (ARL) Robotics Collaborative Technology Alliances (RCTA) program, the Jet Propulsion Laboratory (JPL) was tasked with developing a water and mud detection capability for UGVs. In previous papers, we have reported our progress on detecting and localizing bodies of water in a world model used to plan safe paths (Matthies, Bellutta, & McHenry, 2003;Rankin & Matthies, 2006;Rankin, Matthies, & Huertas, 2004). In this paper, we report our progress on evaluating sensors applicable to mud detection from a UGV.…”

Section: Introductionmentioning

confidence: 99%

Passive sensor evaluation for unmanned ground vehicle mud detection

Rankin¹,

Matthies²

2010

Journal of Field Robotics

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Detecting mud hazards is a significant challenge to unmanned ground vehicle (UGV) autonomous off-road navigation. A military UGV stuck in a body of mud during a mission may need to be sacrificed or rescued, both unattractive options. The Jet Propulsion Laboratory is currently developing a daytime mud detection capability under the U.S. Army Research Laboratory Robotics Collaborative Technology Alliances program using UGVmounted sensors. To perform robust mud detection under all conditions, we expect that multiple sensors will be necessary. A passive mud detection solution is desirable to meet future combat system requirements. To characterize the advantages and disadvantages of candidate passive sensors, outdoor data collections have been performed on wet and dry soil using visible, multispectral (including near-infrared), shortwave infrared, midwave infrared, long-wave infrared, polarization, and stereo sensors. In this paper, we examine the cues for mud detection that each of these sensors provide, along with their deficiencies, and we illustrate localizing detected mud in a world model that can used by a UGV to plan safe paths. We mostly limit our examination to mud detection during the daytime under ideal conditions: isolated wet soil surrounded by dry soil during nominal weather, i.e., no precipitation, calm wind, and near-average temperatures. C

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Detecting water hazards for autonomous off-road navigation

Cited by 37 publications

References 5 publications

The DARPA PerceptOR evaluation experiments

The DARPA PerceptOR evaluation experiments

An Algorithm to Compensate for Road Illumination Changes for AID Systems

Passive sensor evaluation for unmanned ground vehicle mud detection

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