A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

Montalban, Karl; Reymann, Christophe; Atchuthan, Dinesh; Dupouy, Paul-Édouard; Rivière, Nicolas; Lacroix, Simon

doi:10.3390/atmos12060738

Cited by 28 publications

(21 citation statements)

References 23 publications

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“…They declared that all the tested sensors' performance decreased in harsh weather, and it is difficult to say which one is better. Karl et al [104] analysed the intensity and the number of points detected on a calibrated target and in the frustum between the sensor and the target in artificial fog and rain with the LiDARs of Velodyne, Ouster, Livox, Cepton, and AEye in different echo modes. They found that the 1550nm AEye LiDAR allows better quality of point clouds, and a non repetitive scan pattern from the Livox LiDAR shows good performances under fog.…”

Section: Adverse Weather Conditionsmentioning

confidence: 99%

3D ToF LiDAR in Mobile Robotics: A Review

Yang¹,

Li²,

Zhao³

et al. 2022

Preprint

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In the past ten years, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this article systematically reviews and analyzes the use 3D ToF LiDARs in research and industrial applications. The former includes object detection, robot localization, long-term autonomy, LiDAR data processing under adverse weather conditions, and sensor fusion. The latter encompasses service robots, assisted and autonomous driving, and recent applications performed in response to public health crises. We hope that our efforts can effectively provide readers with relevant references and promote the deployment of existing mature technologies in real-world systems.

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Section: Adverse Weather Conditionsmentioning

confidence: 99%

3D ToF LiDAR in Mobile Robotics: A Review

Yang¹,

Li²,

Zhao³

et al. 2022

Preprint

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“…The former is defined by the installed components (i.e., laser) and is reflected in the sensor specifications (i.e., range at 10% remission). Regarding environmental influences, precipitation and visibility (e.g., fog) are named in most cases, which limit the maximum range of the sensors [33,34]. Furthermore, the point density for the detection of an object is influenced by the angle between the object and the LiDAR sensor, due to the angular resolution of the device.…”

Section: Derivation Of Berthing Support Area (Bsa)mentioning

confidence: 99%

“…In this we will consider the possible illumination of vessels based on the sensor specification and setup. The influence of environmental conditions on LiDAR sensors is difficult to estimate and depends on the sensor model [34]. Therefore, the influence of weather is difficult to measure, and we have too little information to consider this for the BSA.…”

Section: Derivation Of Berthing Support Area (Bsa)mentioning

confidence: 99%

Berthing Assistant System Using Reference Points

Mentjes

Wiards

Feuerstack

2022

JMSE

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With more goods to be transported oversea, traffic and vessels’ dimensions increase while berthing areas merely remain constant and thus challenge ship masters and pilots to maneuver in small basins with dense traffic even in bad weather situations. Too fast approaches or steep angles of attack result in damages to fenders, quay walls, or even impact the hull structure. We propose a shore-based, vessel-independent berthing assistant system to support sailors by Reference Points that are aligned to a quay’s meter markings and identify the precise berthing location by measuring distance and approach speed. For this purpose, we define the concept of a Berthing Support Area (BSA), which specifies an area in which, subject to constraints, safe berthing is provided. Within this area there are Reference Points, perpendicular distance measurements at arbitrary positions, which are implemented with a set of LiDAR sensors that have been integrated into the quay wall. In a test campaign with a vessel equipped with DGPS sensors, we sailed seven different maneuvers and evaluated the precision and the accuracy of the Reference Points for speed and distance measurements.

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“…Recent studies have focused on the analysis of a specific noise factor on a sensor type, e.g. rain on LiDAR [4], [5], interference on RADAR [6], [7], fog on camera [8]. However, further investigation of noise factors is required, as the automotive environment is highly complex, swiftly changing, and multiple noise sources can impact the sensor outputs simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Chan

Baris

et al. 2022

IEEE Sensors J.

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Assisted and Automated Driving functions are increasingly deployed to support improved safety, efficiency, and enhance driver experience. However, there are still key technical challenges that need to be overcome as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which in turn can affect the vehicle safety. A framework to analyse noise factor effects on automotive environmental perception sensors has been recently proposed and applied to study the effects of noise factors on LiDAR sensors. This work builds on this framework, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of camera specific noise sources. Moreover, the noise factor analysis has been used to identify two omnipresent and independent noise factors (i.e. obstruction and windshield distortion). These noise factors have been modelled to generate noisy camera data; their impact on the perception step, based on deep neural networks, has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including combination noise factor modelling and testing for performance analysis. Thus, through the findings here, the framework can enhance the use of simulation for development and testing of automated vehicles through careful consideration of the noise factors affecting camera data.

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A Quantitative Analysis of Point Clouds from Automotive Lidars Exposed to Artificial Rain and Fog

Cited by 28 publications

References 23 publications

3D ToF LiDAR in Mobile Robotics: A Review

3D ToF LiDAR in Mobile Robotics: A Review

Berthing Assistant System Using Reference Points

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

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