LiDAR Technologies and Systems

McManamon, Paul

doi:10.1117/3.2518254

Cited by 91 publications

(58 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…On the other hand, noise includes all the unwanted, irregular fluctuations introduced by the signal itself, the detector and the associated electronics which accompany the signal and perturb its detection by obscuring it. Photodetection noise may be due to different parameters well described in [127].…”

Section: Gain and Noisementioning

confidence: 99%

An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019

View full text Add to dashboard Cite

Lidar imaging systems are one of the hottest topics in the optronics industry. The need to sense the surroundings of every autonomous vehicle has pushed forward a race dedicated to deciding the final solution to be implemented. However, the diversity of state-of-the-art approaches to the solution brings a large uncertainty on the decision of the dominant final solution. Furthermore, the performance data of each approach often arise from different manufacturers and developers, which usually have some interest in the dispute. Within this paper, we intend to overcome the situation by providing an introductory, neutral overview of the technology linked to lidar imaging systems for autonomous vehicles, and its current state of development. We start with the main single-point measurement principles utilized, which then are combined with different imaging strategies, also described in the paper. An overview of the features of the light sources and photodetectors specific to lidar imaging systems most frequently used in practice is also presented. Finally, a brief section on pending issues for lidar development in autonomous vehicles has been included, in order to present some of the problems which still need to be solved before implementation may be considered as final. The reader is provided with a detailed bibliography containing both relevant books and state-of-the-art papers for further progress in the subject.

show abstract

Section: Gain and Noisementioning

confidence: 99%

An Overview of Lidar Imaging Systems for Autonomous Vehicles

2019

View full text Add to dashboard Cite

show abstract

“…A 3D flash LiDAR is a LiDAR system that flood illuminates a scene, capturing a broader area at once, than a scanning LiDAR. 6,7 3D flash LiDAR systems typically employ time-of-flight ranging. Through timing synchronization with a laser source, the LiDAR can estimate the time-of-flight of the illumination downrange so long as that object is within its range gate.…”

Section: D Flash Lidarmentioning

confidence: 99%

Windowed region-of-interest non-uniformity correction and range walk error correction of a 3D flash LiDAR camera

et al. 2021

View full text Add to dashboard Cite

We present experimental methods and results for photo-response non-uniformity correction (PRNUC) in range for a 3D flash LiDAR camera from non-optimal static-scene calibration data. Range walk is also corrected. This method breaks up the camera's focal plane array (FPA) into 16 × 16 windowed regions of interest that are incrementally captured and stitched together in post-processing across the entire FPA. The illumination was not uniform, thus requiring additional methods described by our paper to create an acceptable correction. We present the results from a full non-uniformity correction and range walk error correction processed for a set of independently collected validation frames; these validation frames used identical experimental conditions and the same target as was collected for the corrections. We will show that this experimental approach improves range accuracy and range precision of the corrected validation frames despite the sub-optimal conditions of the data used to compute the corrections; the single shot range precision is corrected to 33 cm, as compared to a modeled precision of 15.65 cm, while the accuracy is corrected to 252 cm. This method has implications for simplification of characterization of non-uniformity and range walk error, and its subsequent correction, in 3D flash LiDAR cameras. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…For example, commercially available lidar systems designed for autonomous driving currently utilize high scanning rates and a large field-of-view, requiring high repetition rate lasers with moderate power and ∼200-m maximum distance. [44][45][46] In contrast, scanning linear-mode lidar in 3-D mapping remote sensing applications typically requires a higher power laser and operates at an altitude of ∼1000 to 5000 ft, 46 with operating ranges of ∼1 km or greater. In this section, we demonstrate the feasibility of the proposed architecture by presenting a notional implementation for a remote sensing application.…”

Section: Notional Systemmentioning

confidence: 99%

Hybrid passive polarimetric imager and lidar combination for material classification

et al. 2020

View full text Add to dashboard Cite

We investigate the augmentation of active imaging with passive polarimetric imaging for material classification. Experiments are conducted to obtain a multimodal dataset of lidar reflectivity and polarimetric thermal self-emission measurements against a diverse set of material types. Using the assumption that active lidar imaging can provide high-resolution threedimensional spatial information, a known surface orientation is utilized to enable higher fidelity classification. Machine learning is applied to the dataset of monostatic lidar unidirectional reflectivity and passive longwave infrared degree of linear polarization features for material classification. The hybrid sensor technique can classify materials with 91.1% accuracy even with measurement noise resulting in a signal-to-noise ratio of only 6 dB. The application of the proposed technique is applicable for the classification of hidden objects or could assist existing spatial-based object classification. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

LiDAR Technologies and Systems

Cited by 91 publications

References 0 publications

An Overview of Lidar Imaging Systems for Autonomous Vehicles

An Overview of Lidar Imaging Systems for Autonomous Vehicles

Windowed region-of-interest non-uniformity correction and range walk error correction of a 3D flash LiDAR camera

Hybrid passive polarimetric imager and lidar combination for material classification

Contact Info

Product

Resources

About