A high frequency 3D LiDAR with enhanced measurement density via Papoulis-Gerchberg

Ozbay, B.; Kuzucu, Elvan; Gül, Mustafa; Öztürk, Dilan; Tasci, Muhittin; Arısoy, Ahmet; Sirin, H Onur; Uyanik, Ismail

doi:10.1109/icar.2015.7251509

Cited by 3 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This paper is a follow-up of our recent study (Özbay et al, 2015), in which we showed how an image super-resolution algorithm, P–G, can be utilized to increase the measurement density of range images. In this paper, we extend the P–G algorithm to a dynamic version by using estimates from previous frames to increase performance and reduce convergence time.…”

Section: Discussionmentioning

confidence: 83%

“…Motivated by these problems, our aim in this study is to present a new technique, which is capable of increasing the measurement density of 3D range images with a low time complexity. In a previous work (Özbay et al, 2015), we adopted the Papoulis–Gerchberg (P–G) algorithm (Papoulis, 1975) to enhance the measurement density of single 3D range images that were collected with a 3D range sensor built by rotating a 2D lidar device. However, the slow convergence rate of the P–G algorithm limited the use of proposed method to off-line applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

Kuzucu

Öztürk

Gül

et al. 2018

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

As one of the most popular range detection methods, lidar is commonly used in various robotic applications. Although most robotic platforms easily adopt 2D lidar for range sensing, 3D lidar is rarely used in mobile robots, owing to its high cost. Some methods reported in the literature obtain 3D range information by rotating a single 2D lidar device. However, for most of these methods, there is a trade-off between 3D scan frequency and measurement density. Existing methods discussed in the literature for increasing the measurement density in high-frequency lidar have high time complexity and require certain conditions on data distribution. In a previous work, we showed the usability of an image super-resolution method, the Papoulis–Gerchberg (P–G) algorithm, on range data represented in the form of a greyscale image. However, the low convergence rate of the original P–G algorithm impedes its use for online applications. In this study, we advanced the P–G algorithm to drastically reduce the convergence time and improve performance by utilizing previous range images. The proposed algorithm now supports application on a mobile robot with online measurement density enhancement for 3D range images collected by rotating a 2D lidar device around its pitch axis with a high 3D scan frequency. We show illustrative examples for different scenarios to present the effectiveness of the proposed method on a 3D range sensor mounted on a mobile robot.

show abstract

Section: Discussionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

Kuzucu

Öztürk

Gül

et al. 2018

Transactions of the Institute of Measurement and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the cost of the prototype has also been greatly increased, for a 3D LiDAR is much more expensive. (b) In [100,101], the Papoulis-Gerchberg algorithm is used to process the sparse 3D point cloud to improve its resolution. Originally, the Papoulis-Gerchberg algorithm has been mainly used in image processing, and its function is to convert low-resolution images into high-resolution images.…”

Section: Problem Ii: Real-time Performancementioning

confidence: 99%

A Survey of Low-Cost 3D Laser Scanning Technology

Yuan

Liu

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

By moving a commercial 2D LiDAR, 3D maps of the environment can be built, based on the data of a 2D LiDAR and its movements. Compared to a commercial 3D LiDAR, a moving 2D LiDAR is more economical. A series of problems need to be solved in order for a moving 2D LiDAR to perform better, among them, improving accuracy and real-time performance. In order to solve these problems, estimating the movements of a 2D LiDAR, and identifying and removing moving objects in the environment, are issues that should be studied. More specifically, calibrating the installation error between the 2D LiDAR and the moving unit, the movement estimation of the moving unit, and identifying moving objects at low scanning frequencies, are involved. As actual applications are mostly dynamic, and in these applications, a moving 2D LiDAR moves between multiple moving objects, we believe that, for a moving 2D LiDAR, how to accurately construct 3D maps in dynamic environments will be an important future research topic. Moreover, how to deal with moving objects in a dynamic environment via a moving 2D LiDAR has not been solved by previous research.

show abstract

Analysis of 3D Scan Measurement Distribution with Application to a Multi-Beam Lidar on a Rotating Platform

Morales

Plaza-Leiva

Mandow

et al. 2018

Sensors

View full text Add to dashboard Cite

Multi-beam lidar (MBL) rangefinders are becoming increasingly compact, light, and accessible 3D sensors, but they offer limited vertical resolution and field of view. The addition of a degree-of-freedom to build a rotating multi-beam lidar (RMBL) has the potential to become a common solution for affordable rapid full-3D high resolution scans. However, the overlapping of multiple-beams caused by rotation yields scanning patterns that are more complex than in rotating single beam lidar (RSBL). In this paper, we propose a simulation-based methodology to analyze 3D scanning patterns which is applied to investigate the scan measurement distribution produced by the RMBL configuration. With this purpose, novel contributions include: (i) the adaption of a recent spherical reformulation of Ripley’s K function to assess 3D sensor data distribution on a hollow sphere simulation; (ii) a comparison, both qualitative and quantitative, between scan patterns produced by an ideal RMBL based on a Velodyne VLP-16 (Puck) and those of other 3D scan alternatives (i.e., rotating 2D lidar and MBL); and (iii) a new RMBL implementation consisting of a portable tilting platform for VLP-16 scanners, which is presented as a case study for measurement distribution analysis as well as for the discussion of actual scans from representative environments. Results indicate that despite the particular sampling patterns given by a RMBL, its homogeneity even improves that of an equivalent RSBL.

show abstract

A high frequency 3D LiDAR with enhanced measurement density via Papoulis-Gerchberg

Cited by 3 publications

References 20 publications

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

Enhancing 3D range image measurement density via dynamic Papoulis–Gerchberg algorithm

A Survey of Low-Cost 3D Laser Scanning Technology

Analysis of 3D Scan Measurement Distribution with Application to a Multi-Beam Lidar on a Rotating Platform

Contact Info

Product

Resources

About