Range Information Characterization of the Hokuyo UST-20LX LIDAR Sensor

Cooper, Matthew A.; Raquet, John; Patton, Rick

doi:10.3390/photonics5020012

Cited by 29 publications

(18 citation statements)

References 32 publications

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“…The intensity curve shown in (1) [15] reveals that the actual intensity value depends on the angle of incidence and object surface reflectivity [15] [16] [17]. The variables…”

Section: B Preliminariesmentioning

confidence: 99%

Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds

Park

Kim

2020

IEEE Access

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LiDAR sensors have the advantage of being able to generate high-resolution imaging quickly during both day and night; however, their performance is severely limited in adverse weather conditions such as snow, rain, and dense fog. Consequently, many researchers are actively working to overcome these limitations by applying sensor fusion with radar and optical cameras to LiDAR. While studies on the denoising of point clouds acquired by LiDAR in adverse weather have been conducted recently, the results are still insufficient for application to autonomous vehicles because of speed and accuracy performance limitations. Therefore, we propose a new intensity-based filter that differs from the existing distance-based filter, which limits the speed. The proposed method showed overwhelming performance advantages in terms of both speed and accuracy by removing only snow particles while leaving important environmental features. The intensity criteria for snow removal were derived based on an analysis of the properties of laser light and snow particles. INDEX TERMS snow noise removal, desnowing, autonomous vehicle, LiDAR point cloud filtering

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“…The intensity curve shown in (1) [15] reveals that the actual intensity value depends on the angle of incidence and object surface reflectivity [15] [16] [17]. The variables…”

Section: B Preliminariesmentioning

confidence: 99%

Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds

Park

Kim

2020

IEEE Access

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“…The performance of the Hokuyo URG-04LX was evaluted using a similar rail setup [22] as well as the SICK LMS200 [23]. The Hokuyo UST-20LX was also characterized over near distances [24] as well as the SICK LMS511-20100Pro [25]. Obstacle detection capabilities were investigated for the Hokuyo UTM-30LX [26], Hokuyo URG-04LX [22] and SICK LMS200 [23], while the mapping capabilities of the SICK LMS511 was the focus of another study [25].…”

Section: A Lidar Testingmentioning

confidence: 99%

“…By segmenting points from each target as shown on the left of Figure 14a, the accuracy and precision analysis can be performed for each reflective target. The impact of surface reflectivity on LiDAR measurements is one of the more extensively studied subjects, for example by using comparing different color cardboard [18], [24], different types of metals [27], or comparing a variety of materials [25]. This work is limited to three targets, one with very high reflectivity (diamond target) and one with very low reflectivity (velvet target).…”

Section: ) Impact Of Surface Reflectivitymentioning

confidence: 99%

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving

et al. 2020

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Automated vehicle technology has recently become reliant on 3D LiDAR sensing for perception tasks such as mapping, localization and object detection. This has led to a rapid growth in the LiDAR manufacturing industry with several competing makers releasing new sensors regularly. With this increased variety of LiDARs, each with different properties such as number of laser emitters, resolution, field-of-view, and price tags, a more in-depth comparison of their characteristics and performance is required. This work compares 10 commonly used 3D LiDARs, establishing several metrics to assess their performance. Various outstanding issues with specific LiDARs were qualitatively identified. The accuracy and precision of individual LiDAR beams and accumulated point clouds are evaluated in a controlled environment at distances from 5 to 180 meters. Reflective targets were used to characterize intensity patterns and quantify the impact of surface reflectivity on accuracy and precision. A vehicle and pedestrian mannequin were also used as additional targets of interest. A thorough assessment of these LiDARs is given with their potential applicability for automated driving tasks. The data collected in these experiments and analysis tools are all shared openly.

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“…When a laser spot is located at the very edge of an object, the measured range is that of a combination of the foreground object and the background object, i.e., the range falls in between the distances to the foreground and background objects. This condition is called "mixed pixels" (Cooper et al, 2018).…”

Section: Problem Of Pixel Mixingmentioning

confidence: 99%

Systematic Evaluation and Characterization of 3d Solid State Lidar Sensors for Autonomous Ground Vehicles

Aijazi

Laurent

Trassoudaine

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D LiDAR sensors play an important part in several autonomous navigation and perception systems with the technology evolving rapidly over time. This work presents the preliminary evaluation results of a 3D solid state LiDAR sensor. Different aspects of this new type of sensor are studied and their data are characterized for their effective utilization for object detection for the application of Autonomous Ground Vehicles (AGV). The paper provides a set of evaluations to analyze the characterizations and performances of such LiDAR sensors. After characterization of the sensor, the performance is also evaluated in real environment with the sensors mounted on top of a vehicle and used to detect and classify different objects using a state-of-the-art Super-Voxel based method. The 3D point cloud obtained from the sensor is classified into three main object classes “Building”, “Ground” and “Obstacles”. The results evaluated on real data, clearly demonstrate the applicability and suitability of the sensor for such type of applications.

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Range Information Characterization of the Hokuyo UST-20LX LIDAR Sensor

Cited by 29 publications

References 32 publications

Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds

Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds

Performance Analysis of 10 Models of 3D LiDARs for Automated Driving

Systematic Evaluation and Characterization of 3d Solid State Lidar Sensors for Autonomous Ground Vehicles

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