Evidence map›Paper›PMID 39599045›Full record

ReviewSensors (Basel, Switzerland)2024

Advances in LiDAR Hardware Technology: Focus on Elastic LiDAR for Solid Target Scanning.

Wentao Li, Tianyun Shi, Rui Wang, Jingjie Yang, Zhen Ma, Wanpeng Zhang, Huijin Fu, Pengyue Guo

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Wentao LiPostgraduate Department, China Academy of Railway Sciences, Beijing 100081, China.ORCID 0000-0001-7058-4951
Tianyun ShiScience and Information Department, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Rui WangInstitute of Computing Technologies, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Jingjie YangRailway Science & Technology Research & Development Center, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Zhen MaInstitute of Computing Technologies, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Wanpeng ZhangInstitute of Computing Technologies, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Huijin FuInstitute of Computing Technologies, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.
Pengyue GuoInstitute of Computing Technologies, China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China.

Funding

National Natural Science Foundation of China U2268217
6 · The paper itself

Abstract

This paper explores the development of elastic LiDAR technology, focusing specifically on key components relevant to solid target scanning applications. By analyzing its fundamentals and working mechanisms, the advantages of elastic LiDAR for precise measurement and environmental sensing are demonstrated. This paper emphasizes innovative advances in emitters and scanning systems, and examines the impact of optical design on performance and cost. Various ranging methods are discussed. Practical application cases of elastic LiDAR are presented, and future trends and challenges are explored. The purpose of this paper is to provide a comprehensive perspective on the technical details of elastic LiDAR, the current state of application, and future directions. All instances of "LiDAR" in this paper specifically refer to elastic LiDAR.

Indexed as

detectorselastic LiDARemitterslaser rangingscanning and optical modulessolid target scanning

Identifiers

PMID39599045
PMCPMC11597980

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.