Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Pełka, Michał; Bedkowski, Janusz

doi:10.3390/s21196501

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…With the advent of Lidar drones, Lidar (Light Detection and Ranging) technologywhich has historically been utilized in portable sensors, aerial sensors aboard aircraft, and satellite-based systems-has experienced notable developments [11]. These drones are outfitted with Lidar sensors, which measure distances with astounding accuracy using laser light.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Land Area Mapping and Volume Calculations using Drone Lidar Livox Mid-40 Data with the Downsampling Method

Manggala,

Ahyudanari,

Cahyadi

et al. 2024

BIO Web Conf.

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One reason for the growing acceptance of 3D point cloud-based research and applications is the quick advancement of 3D scanning technologies. However, there are still a number of serious issues that have an impact on point cloud utilization performance. Among these difficulties are controlling the quantity of points, irregular point density, and a deficiency of location proximity data. In this study, we use Livox Mid-40 Drone Lidar Data and a downsampling technique to compute land area and volume. However, it can be highly challenging and time-consuming to extract usable information from enormous amounts of gathered data. Motivated by these results, this study recommends using downsampling approaches to minimize the size of the final data set while preserving data integrity, which will facilitate and expedite. The Livox Mid-40 Lidar Drone data was optimal at 00:00:30 with a flying height of 75,719 meters and a measurement diameter of 50.3 meters. By using downsampling techniques, the number of points can be reduced by up to 40 percent from the previous number of data points. Meanwhile, the data size can be 10 percent smaller than the original data. To calculate the area of land of the same size, there is a difference of 0.53 square meters. Meanwhile, for the calculation of cubic volume, there is a difference of 1.63 cubic meters.

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Section: Introductionmentioning

confidence: 99%

Optimization of Land Area Mapping and Volume Calculations using Drone Lidar Livox Mid-40 Data with the Downsampling Method

Manggala,

Ahyudanari,

Cahyadi

et al. 2024

BIO Web Conf.

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show abstract

“…The authors also described reshaping the radial field of view into a new narrowed one. The problem of such calibration is not discussed [ 21 ], but we addressed it in our previous work [ 22 ], which is extended for the calibration of the 3D sensor presented in this paper. The idea of reshaping a field of view using mirrors or prisms can be implemented in several applications.…”

Section: Introductionmentioning

confidence: 99%

Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector

Bedkowski

Pełka²

2023

Sensors

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This paper describes an affordable robotic mobile 3D mapping system. It is built with Livox Mid−40 lidar with a conic field of view extended by a custom rotating planar reflector. This 3D sensor is compared with the more expensive Velodyne VLP 16 lidar. It is shown that the proposed sensor reaches satisfactory accuracy and range. Furthermore, it is able to preserve the metric accuracy and non−repetitive scanning pattern of the unmodified sensor. Due to preserving the non−repetitive scan pattern, our system is capable of covering the entire field of view of 38.4 × 360 degrees, which is an added value of conducted research. We show the calibration method, mechanical design, and synchronization details that are necessary to replicate our system. This work extends the applicability of solid−state lidars since the field of view can be reshaped with minimal loss of measurement properties. The solution was part of a system that was evaluated during the 3rd European Robotics Hackathon in the Zwentendorf Nuclear Power Plant. The experimental part of the paper demonstrates that our affordable robotic mobile 3D mapping system is capable of providing 3D maps of a nuclear facility that are comparable to the more expensive solution.

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Section: Introductionmentioning

confidence: 99%

“…Recently, a paper [ 14 ] by Pełka et al described a calibration procedure for a 3D flash Lidar where the FOV is reshaped by six plane mirrors into a shape that is similar to that of a 3D spinning Lidar. Their presented calibration method does not use any fiducials on the mirrors and relies on “pairs of the nearest neighborhood points that were reflected by a different mirror” [ 14 ] (p. 9). This approach cannot succeed with data from 2D-Lidar sensors, as in our proposed configuration, because there can be no more than one of these point pairs at the position where two scan lines of the 2D-Lidar cross each other.…”

Section: Introductionmentioning

confidence: 99%

Design and Calibration of Plane Mirror Setups for Mobile Robots with a 2D-Lidar

Kibii

Dreher²,

Wormser³

et al. 2022

Sensors

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Lidar sensors are widely used for environmental perception on autonomous robot vehicles (ARV). The field of view (FOV) of Lidar sensors can be reshaped by positioning plane mirrors in their vicinity. Mirror setups can especially improve the FOV for ground detection of ARVs with 2D-Lidar sensors. This paper presents an overview of several geometric designs and their strengths for certain vehicle types. Additionally, a new and easy-to-implement calibration procedure for setups of 2D-Lidar sensors with mirrors is presented to determine precise mirror orientations and positions, using a single flat calibration object with a pre-aligned simple fiducial marker. Measurement data from a prototype vehicle with a 2D-Lidar with a 2 m range using this new calibration procedure are presented. We show that the calibrated mirror orientations are accurate to less than 0.6° in this short range, which is a significant improvement over the orientation angles taken directly from the CAD. The accuracy of the point cloud data improved, and no significant decrease in distance noise was introduced. We deduced general guidelines for successful calibration setups using our method. In conclusion, a 2D-Lidar sensor and two plane mirrors calibrated with this method are a cost-effective and accurate way for robot engineers to improve the environmental perception of ARVs.

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Calibration of Planar Reflectors Reshaping LiDAR’s Field of View

Cited by 4 publications

References 37 publications

Optimization of Land Area Mapping and Volume Calculations using Drone Lidar Livox Mid-40 Data with the Downsampling Method

Optimization of Land Area Mapping and Volume Calculations using Drone Lidar Livox Mid-40 Data with the Downsampling Method

Affordable Robotic Mobile Mapping System Based on Lidar with Additional Rotating Planar Reflector

Design and Calibration of Plane Mirror Setups for Mobile Robots with a 2D-Lidar

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