Fog removal using laser beam penetration, laser intensity, and geometrical features for 3D measurements in fog-filled room

Shamsudin, Abu Ubaidah; Ohno, Kazunori; Westfechtel, Thomas; Suzuki, Takahiro; Okada, Yoshito; Tadokoro, Satoshı

doi:10.1080/01691864.2016.1164620

Cited by 28 publications

(24 citation statements)

References 5 publications

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“…An approach for removing near field reflections caused by fog is proposed in [14] for one static scene. The algorithm is based on the light beam penetration, the intensity and geometrical features for measurements recorded in a fog-filled room with 2 and 6 m visibility.…”

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

“…The algorithm is based on the light beam penetration, the intensity and geometrical features for measurements recorded in a fog-filled room with 2 and 6 m visibility. Since the data sets used in [14] only contains a single static scene, the reported results might be affected by overfitting of the underlying classifier.…”

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

“…Lidar sensor performance significantly depends on the environmental conditions as demonstrated in [4], [6], [10]- [14]. Thus, it is fundamental to recognize and quantify the impact of current weather on the lidar performance in order to develop robust perception and thus fusion algorithms.…”

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

“…reduction of speed during precipitation and wet road). In comparison to [14], it is not the goal to detect at point level whether the detection was caused by adverse weather or not. Since disturbance points caused by fog or rain are not the main influencing factor, but rather the enormous reduction of sensor performance in terms of range.…”

Section: B Main Contributionmentioning

confidence: 99%

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Weather Influence and Classification with Automotive Lidar Sensors

Heinzler

Schindler

Seekircher

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

172

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Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception. Typically, perception algorithms are trained to only detect moving and static objects as well as ground estimation, but intentionally ignore weather effects to reduce false detections. In this work, we present an in-depth analysis of automotive lidar performance under harsh weather conditions, i.e. heavy rain and dense fog. An extensive data set has been recorded for various fog and rain conditions, which is the basis for the conducted indepth analysis of the point cloud under changing environmental conditions. In addition, we introduce a novel approach to detect and classify rain or fog with lidar sensors only and achieve an mean union over intersection of 97.14 % for a data set in controlled environments. The analysis of weather influences on the performance of lidar sensors and the weather detection are important steps towards improving safety levels for autonomous driving in adverse weather conditions by providing reliable information to adapt vehicle behavior.

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Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

Section: A Lidar Sensors In Adverse Weather Conditionsmentioning

confidence: 99%

Section: B Main Contributionmentioning

confidence: 99%

See 2 more Smart Citations

Weather Influence and Classification with Automotive Lidar Sensors

Heinzler

Schindler

Seekircher

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

172

View full text Add to dashboard Cite

show abstract

“…Actual smoke at fire scenes is a collection of liquid particles and solid particles, and its diameter is of the order of micrometers [13]. The fog machine in the box environment generates liquid particles of the order of micrometers [14], and the wavelength of 10 kHz-45 kHz sound is of the order of millimeters. They mean that the machine-generated particles have the almost same diameter as actual smoke.…”

Section: Methodsmentioning

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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