Evidence map›Paper›PMID 39506965›Full record

ArticleHeliyon2024

RLCFormer: Automatic roadside LiDAR-Camera calibration framework with transformer.

Rui Tian, Xuefeng Bao, Yunli Chen, Feng Liu, Yiqiang Zhen, Yong Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 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. Article
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

6 authors.

Rui TianFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Xuefeng BaoFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Yunli ChenFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
Feng LiuSchool of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China.
Yiqiang ZhenDFH Satellite Co., Ltd., Beijing, 100094, China.
Yong LiFaculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

LiDAR-Camera fusion is pivotal for perceiving and understanding complex traffic environments, particularly valuable in autonomous driving and traffic monitoring. Traditional calibration algorithms, primarily designed for onboard sensors, are inadequate for roadside setups where sensors are positioned higher and more dispersed. To address this challenge, we introduce the RLCFormer, a Transformer-based network specifically tailored for precise calibration of roadside sensors. This method innovatively integrates depth and RGB features, utilizing correlation layers and a Transformer decoder to accurately match features across modalities. Evaluated on the DAIR-V2X-I Roadside 3D detection dataset, the RLCFormer achieves an average translation error of 3.3187 cm and a rotation error of 0.0469°, surpassing existing methods. Our approach significantly enhances scene representation and calibration precision, offering a robust solution for roadside sensor calibration and advancing the state of the art in sensor fusion technology.

Indexed as

Calibration and identificationComputer vision for transportationLiDAR-camera systemsRoadside traffic monitoringSensor fusion

Identifiers

PMID39506965
PMCPMC11538635

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.