The Development of a 3D LADAR Simulator Based on a Fast Target Impulse Response Generation Approach

Al-Temeemy, Ali A.

doi:10.1007/s13319-017-0142-y

Cited by 5 publications

(3 citation statements)

References 34 publications

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“…LIDARs were also modelled and integrated in simulation softwares. Al-Temeemy [13] and Kruapech et al [14] modelled a LIDAR return waveform using a Gaussian beam representation to compute the laser beam propagation. They however did not consider the incidence angle.…”

Section: Related Workmentioning

confidence: 99%

Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping

Laconte

Deschênes

Labussière

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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In a context of 3D mapping, it is very important to obtain accurate measurements from sensors. In particular, Light Detection And Ranging (LIDAR) measurements are typically treated as a zero-mean Gaussian distribution. We show that this assumption leads to predictable localisation drifts, especially when a bias related to measuring obstacles with high incidence angles is not taken into consideration. Moreover, we present a way to physically understand and model this bias, which generalizes to multiple sensors. Using an experimental setup, we measured the bias of the Sick LMS151, Velodyne HDL-32E, and Robosense RS-LiDAR-16 as a function of depth and incidence angle, and showed that the bias can reach 20 cm for high incidence angles. We then used our model to remove the bias from the measurements, leading to more accurate maps and a reduced localisation drift.

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Section: Related Workmentioning

confidence: 99%

Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping

Laconte

Deschênes

Labussière

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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“…Another study focused on development of a new laser detection and ranging (LADAR) simulator created in MATLAB [ 21 ] with the focus mainly on accuracy of the simulation by properly simulating the important aspects of the laser beam while maintaining a good calculation speed. The intended target audience of this simulator are developers of LADAR systems who need to devise and configure their data processing algorithms in an effective way.…”

Section: Introductionmentioning

confidence: 99%

Using Virtual Scanning to Find Optimal Configuration of a 3D Scanner Turntable for Scanning of Mechanical Parts

Kot

Bobovský

Heczko

et al. 2021

Sensors

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The article describes a method of simulated 3D scanning of triangle meshes based on ray casting which is used to find the optimal configuration of a real 3D scanner turntable. The configuration include the number of scanners, their elevation above the rotary table and the number of required rotation steps. The evaluation is based on the percentage of the part surface covered by the resulting point cloud, which determines the ability to capture all details of the shape. Principal component analysis is used as a secondary criterion to also evaluate the ability to capture the overall general proportions of the model.

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“…The division of the process during simulation.Figure adapted from(Carlsson et al 2001; Richmond and Cain 2010;Al-Temeemy 2017).…”

mentioning

confidence: 99%

Computational Intelligence for Simulating a LiDAR Sensor

Castaño

Beruvides

Villalonga

et al. 2019

Sensor Systems Simulations

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In this chapter, an overview of some of the most commonly Computational Intelligence techniques used to provide new capabilities to sensor networks in Cyber-Physical and Internet-of-Things environments, and for verifying and evaluating the reliability issues of sensor networks is presented. Nowadays, on-chip Light Detection and Ranging (LiDAR) concept has driven a great technological challenge into sensor networks application for Cyber-Physical and Internet-of-Things systems. Therefore, the modelling and simulation of a LiDAR sensor networks is also included in this chapter that is structured as follows. First, a brief description of the theoretical modelling of the mathematical principle of operation is outlined. Subsequently, a review of the state-of-art of Computational Intelligence techniques in sensor system simulations is explained. Likewise, a use case of applying computational intelligence techniques to LiDAR sensor networks in a Cyber-Physical System environment is presented. In this use case, a model library with four specific Artificial Intelligence-based methods is also designed based on sensory information database provided by the LiDAR simulation. Some of them are multi-layer perceptron neural network, a self-organization map, a support vector machine and a k-nearest neighbours. The results demonstrate the suitability of using Computational Intelligence methods to increase the reliability of sensor networks when addressing the key challenges of safety and security in automotive applications.

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The Development of a 3D LADAR Simulator Based on a Fast Target Impulse Response Generation Approach

Cited by 5 publications

References 34 publications

Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping

Lidar Measurement Bias Estimation via Return Waveform Modelling in a Context of 3D Mapping

Using Virtual Scanning to Find Optimal Configuration of a 3D Scanner Turntable for Scanning of Mechanical Parts

Computational Intelligence for Simulating a LiDAR Sensor

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