Open-Source LiDAR Time Synchronization System by Mimicking GNSS-clock

Faizullin, Marsel; Kornilova, Anastasiia; Ferrer, Gonzalo

doi:10.1109/ispcs55791.2022.9918446

Cited by 13 publications

(4 citation statements)

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“…In these instances, an accurate time source is distributed to the network server to ensure that time error is maintained within the required offset bound δ. Similarly to the approach presented in [30][31][32], we conducted hardware experiments to obtain statistical results of time errors, for which the distribution is modeled as a normal distribution:…”

Section: Preliminariesmentioning

confidence: 99%

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

Lee,

Yuan,

Petrunin

et al. 2024

Drones

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This paper investigates the influence of time synchronization on sensor fusion and target tracking. As a benchmark, we design a target tracking system based on track-to-track fusion architecture. Heterogeneous sensors detect targets and transmit measurements through a communication network, while local tracking and track fusion are performed in the fusion center to integrate measurements from these sensors into a fused track. The time synchronization error is mathematically modeled, and local time is biased from the reference clock during the holdover phase. The influence of the time synchronization error on target tracking system components such as local association, filtering, and track fusion is discussed. The results demonstrate that an increase in the time synchronization error leads to deteriorating association and filtering performance. In addition, the results of the simulation study validate the impact of the time synchronization error on the sensor network.

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Section: Preliminariesmentioning

confidence: 99%

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

Lee,

Yuan,

Petrunin

et al. 2024

Drones

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“…GNSS timing is widely recognized as the most ubiquitous, precise, and mature synchronization technique. However, its functionality is limited in GNSS-denied areas, and its precision might experience drift after prolonged signal losses [127]. In such scenarios, the concept of mimicking a GNSS-clock can offer a viable alternative [41], [127].…”

Section: B Hardware Synchronizationmentioning

confidence: 99%

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

Liu,

Fu,

Qin

et al. 2024

IEEE Trans. Intell. Veh.

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The development of intelligent Vehicles (IVs) requires extensive standard datasets for training, benchmarking, and improvement. Autonomous Navigation and Mapping (ANM), as a critical technology for IVs, imposes exceptionally high demands on dataset construction. This is significant in its requirements for comprehensive sensor calibration, precise time synchronization, and accurate generation of ground truth. Besides, the whole construction workflow also demands intricate knowledge and sophisticated practices, necessitating lengthy learning curves for researchers to attain proficiency. The above challenges have led to a slow production of qualified datasets, directly constraining the advancement of ANM. However, so far, an investigation focused on a mature construction methodology of ANM dataset is still missing. This paper strives to fill the gap. Specifically, based on our systematic reviews and extensive practices, for the first time, a full-stack construction methodology of ANM dataset is proposed, including modules of platform construction, sensor calibration, time synchronization, ground truth generation, synthetic data production, and benchmark criteria, with detailed techniques and methodological routes provided in each step. Several long-standing issues are resolved within the methodology. Importantly, we introduce versatile calibration and synchronization frameworks that attain up to us-level and mmlevel precision. Besides, we propose a full-scenario ground truth system that can generate scene-map and trajectory at cm-level accuracy. To verify the effectiveness of our methodology, we design a high-quality dataset and benchmark multiple state-ofthe-art algorithms on it. The successful workflow demonstrates that our methodology can significantly reduce the research threshold and help individuals and institutions to construct datasets in a standardized way.

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“…Moving to the next step (in the middle of Figure 2), there are different approaches to synchronize sensors in time. Papers like (Faizullin et al 2021) show that synchronization on a hardware level can be very accurate (less than 1 ms). On the flip side, though, this approach requires a custom design of a synchronization system, often including an additional microcontroller and bespoke wiring between sensors.…”

Section: Data Collection System Of the Hand-held Scannermentioning

confidence: 99%

ConSLAM: Construction Data Set for SLAM

Trzeciak

Pluta

Fathy

et al. 2023

J. Comput. Civ. Eng.

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This paper presents a dataset collected periodically on a construction site. The dataset aims to evaluate the performance of SLAM algorithms used by mobile scanners or autonomous robots. It includes ground-truth scans of a construction site collected using a terrestrial laser scanner along with five sequences of spatially registered and time-synchronized images, LiDAR scans and inertial data coming from our prototypical hand-held scanner. We also recover the ground-truth trajectory of the mobile scanner by registering the sequential LiDAR scans to the ground-truth scans and show how to use a popular software package to measure the accuracy of SLAM algorithms against our trajectory automatically. To the best of our knowledge, this is the first publicly accessible dataset consisting of periodically collected sequential data on a construction site.

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Open-Source LiDAR Time Synchronization System by Mimicking GNSS-clock

Cited by 13 publications

References 0 publications

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

Impact Analysis of Time Synchronization Error in Airborne Target Tracking Using a Heterogeneous Sensor Network

Standard Datasets for Autonomous Navigation and Mapping: A Full-Stack Construction Methodology

ConSLAM: Construction Data Set for SLAM

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