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

Faizullin, Marsel; Kornilova, Anastasiia; Ferrer, Gonzalo

doi:10.48550/arxiv.2107.02625

Cited by 3 publications

(3 citation statements)

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“…Before introducing the labelling process, we first discuss the time synchronisation problem. Different sensors can be synchronised using pulse-per-second (PPS) triggering signals from the GNSS receiver [71]. However, in most of the radar datasets, sensors differ in their triggering time and sampling frequency.…”

Section: Data Labellingmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

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Section: Data Labellingmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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“…Instead of handling sensor timing compensation after acquisition, a dedicated hardware solution can trigger and time stamp sensor data from a single clock. Different designs based on field-programmable gate arrays (FPGAs) or microcontroller units (MCUs) offer more or less freedom to be extended to a custom sensor setup and GNSS synchronization (Kais et al, 2006;Ding et al, 2008;Huck et al, 2011;Nikolic et al, 2014;Albrektsen and Johansen, 2018;Tschopp et al, 2020;Faizullin et al, 2021b;Faizullin et al, 2021a). We base our work on the open-source VersaVIS (Tschopp et al, 2020) sensor synchronization board.…”

Section: Related Workmentioning

confidence: 99%

Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Bähnemann¹,

Lawrance²,

Streichenberg³

et al. 2022

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Ground-penetrating radar mounted on a micro aerial vehicle (MAV) is a promising tool to assist humanitarian landmine clearance. However, the quality of synthetic aperture radar images depends on accurate and precise motion estimation of the radar antennas as well as generating informative viewpoints with the MAV. This paper presents a complete and automatic airborne ground-penetrating synthetic aperture radar (GPSAR) system. The system consists of a spatially calibrated and temporally synchronized industrial grade sensor suite that enables navigation above ground level, radar imaging, and optical imaging. A custom mission planning framework allows generation and automatic execution of stripmap and circular GPSAR trajectories controlled above ground level as well as aerial imaging survey flights. A factor graph based state estimator fuses measurements from dual receiver real-time kinematic (RTK) global navigation satellite system (GNSS) and an inertial measurement unit (IMU) to obtain precise, high-rate platform positions and orientations. Ground truth experiments showed sensor timing as accurate as 0.8 µs and as precise as 0.1 µs with localization rates of 1 kHz. The dual position factor formulation improves online localization accuracy up to 40 % and batch localization accuracy up to 59 % compared to a single position factor with uncertain heading initialization. Our field trials validated a localization accuracy and precision that enables coherent radar measurement addition and detection of radar targets buried in sand. This validates the potential as an aerial landmine detection system.

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“…However, in the GPS rejected region, timing accuracy was seriously affected by the signal problem. To overcome this drawback, Marsel Faizullin et al [10] proposed a microcontroller-based platform for GPS signal emulation that can be extended to arbitrary multi-sensors beyond LiDAR-imu. Hannes Sommer et al [11] used simple external devices, including LED (for camera) or photodiodes (for LiDAR), to achieve temporal calibration.…”

Section: Related Workmentioning

confidence: 99%

Temporal and Spatial Online Integrated Calibration for Camera and LiDAR

Wang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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While camera and LiDAR are widely used in most of the assisted and autonomous driving systems, only a few works have been proposed to associate the temporal synchronization and extrinsic calibration for camera and LiDAR which are dedicated to online sensors data fusion. The temporal and spatial calibration technologies are facing the challenges of lack of relevance and real-time. In this paper, we introduce the pose estimation model and environmental robust line features extraction to improve the relevance of data fusion and instant online ability of correction. Dynamic targets eliminating aims to seek optimal policy considering the correspondence of point cloud matching between adjacent moments. The searching optimization process aims to provide accurate parameters with both computation accuracy and efficiency. To demonstrate the benefits of this method, we evaluate it on the KITTI benchmark with ground truth value. In online experiments, our approach improves the accuracy by 38.5% than the soft synchronization method in temporal calibration. While in spatial calibration, our approach automatically corrects disturbance errors within 0.4 second and achieves an accuracy of 0.3-degree. This work can promote the research and application of sensor fusion.

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

Cited by 3 publications

References 0 publications

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Under the Sand: Navigation and Localization of a Micro Aerial Vehicle for Landmine Detection with Ground-Penetrating Synthetic Aperture Radar

Temporal and Spatial Online Integrated Calibration for Camera and LiDAR

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