Scan Pattern Characterization of Velodyne VLP-16 Lidar Sensor for UAS Laser Scanning

Lassiter, H. Andrew; Whitley, Travis; Wilkinson, Benjamin; Abd-Elrahman, Amr

doi:10.3390/s20247351

Cited by 8 publications

(4 citation statements)

References 17 publications

(19 reference statements)

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“…This proposed algorithm with NLOS bias estimation is named NLOS-robust UWB-inertial odometry, ‘NR-UIO’ in short. GT was measured through a LiDAR sensor, Velodyne PUCK LiDAR (VLP-16) [ 32 ], installed in the UAV, and its

position was measured using the Google Cartographer SLAM algorithm [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Hyun

Myung

2021

Sensors

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Recently, technology utilizing ultra-wideband (UWB) sensors for robot localization in an indoor environment where the global navigation satellite system (GNSS) cannot be used has begun to be actively studied. UWB-based positioning has the advantage of being able to work even in an environment lacking feature points, which is a limitation of positioning using existing vision- or LiDAR-based sensing. However, UWB-based positioning requires the pre-installation of UWB anchors and the precise location of coordinates. In addition, when using a sensor that measures only the one-dimensional distance between the UWB anchor and the tag, there is a limitation whereby the position of the robot is solved but the orientation cannot be acquired. To overcome this, a framework based on an interacting multiple model (IMM) filter that tightly integrates an inertial measurement unit (IMU) sensor and a UWB sensor is proposed in this paper. However, UWB-based distance measurement introduces large errors in multipath environments with obstacles or walls between the anchor and the tag, which degrades positioning performance. Therefore, we propose a non-line-of-sight (NLOS) robust UWB ranging model to improve the pose estimation performance. Finally, the localization performance of the proposed framework is verified through experiments in real indoor environments.

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position was measured using the Google Cartographer SLAM algorithm [ 33 ].…”

Section: Resultsmentioning

confidence: 99%

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Hyun

Myung

2021

Sensors

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“…As shown in Figure 3, the angle of direction of shift is not entirely parallel to the floor as the distance increases. Each firing of the laser scans creates a path of hyperbola across the plane and a slight affine distortion to each hyperbola in the forward motion of the scanner [50]. In this paper, we proposed to use only the 1 • angle for both experiments because it only focuses on the chosen region of interest, which is the ankle of the subject.…”

Section: Discussionmentioning

confidence: 99%

Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point

Kadir

Muguro

Matsushita

et al. 2022

Signals

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Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.

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“…Equations 7-13 [16] are used to determine the ground distance covered by the point cloud. To determine the width (W) coverage of the point cloud trigonometry is used to solve W because the height (H) is known and the vertical field of view of the LiDAR is also known.…”

Section: Max Range 100 M Horizontal Fov 360˚ Vertical Fov 30˚mentioning

confidence: 99%

3D mapping and photogrammetry sensor payload for unmanned aerial vehicles

Purdon,

van Niekerk,

Phillips

et al. 2023

MATEC Web Conf.

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Unmanned Aerial Vehicles (UAVs) have shown great potential for data collection and monitoring of areas. Sensors such as LiDARs and cameras can be used on UAVs for high-resolution data collection and used for various applications like Digital Surface Models (DSM), photogrammetry, inspection systems, and maintenance applications. This paper describes the design and implementation of a sensor payload for UAVs using a LiDAR sensor, camera, Inertial Measurement Unit (IMU), and Global Positioning System (GPS). The payload collects data that can be used to georeference LiDAR and camera data, which can later be used to generate a georeferenced map and perform object detection and classification.

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Scan Pattern Characterization of Velodyne VLP-16 Lidar Sensor for UAS Laser Scanning

Cited by 8 publications

References 17 publications

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

NR-UIO: NLOS-Robust UWB-Inertial Odometry Based on Interacting Multiple Model and NLOS Factor Estimation

Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point

3D mapping and photogrammetry sensor payload for unmanned aerial vehicles

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