3D Mobile Mapping of the Environment using Imaging Radar Sensors

被引：2

作者：

Glira, Philipp ^{[1
]}

Weidinger, Christoph ^{[2
]}

Kadiofsky, Thomas ^{[2
]}

Pointner, Wolfgang ^{[2
]}

Olsbock, Katharina ^{[2
]}

Zinner, Christian ^{[2
]}

Doostdar, Masrur ^{[3
]}

机构：

[1] AIT Austrian Inst Techn, Vienna, Austria

[2] AIT, Vienna, Austria

[3] Indurad GmbH, Aachen, Germany

来源：

2022 IEEE RADAR CONFERENCE (RADARCONF'22) | 2022年

关键词：

mapping; robotics; target modeling; calibration;

D O I：

10.1109/RADARCONF2248738.2022.9763906

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

For 3D sensing of the environment, lidar sensors and stereo cameras are mostly used. These sensors work best in an ideal environment, i.e. with clear visibility. Radar, however, is widely unaffected by external influences (like rain, snow, fog, or dust) due to its longer wavelength, e.g. 4mm@77GHz. In this work we investigate the capabilities of an FMCW imaging radar sensor mounted on a mobile platform for 3D topographic mapping. We describe our radar processing pipeline and introduce thereby a new method to extract and model radar targets from radar images and a new method to estimate the extrinsic calibration of radar sensors. We demonstrate these developments by generating a radar-based 3D point cloud and multi-layer grid map of a quarry.

引用

页数：7

共 19 条

[1] Baker Christopher J., 2013, Principles of Modern Radar: Volume 3: Radar Applications, P17, DOI 10.1049/SBRA503E_ch2
[2] BESL PJ, 1992, P SOC PHOTO-OPT INS, V1611, P586, DOI 10.1117/12.57955
[3] Radar SLAM using visual features
Callmer, Jonas
Tornqvist, David
Gustafsson, Fredrik
Svensson, Henrik
Carlbom, Pelle
[J]. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2011,
[4] Cen SH, 2018, IEEE INT CONF ROBOT, P6045, DOI 10.1109/ICRA.2018.8460687
[5] Domhof J, 2019, IEEE INT CONF ROBOT, P8107, DOI [10.1109/icra.2019.8794186, 10.1109/ICRA.2019.8794186]
[6] El Natour G, 2015, IEEE INT CONF ROBOT, P2084, DOI 10.1109/ICRA.2015.7139473
[7] Foessel A., 2002, SCENE MODELING MOTIO
[8] Radar and LiDAR Sensorfusion in Low Visibility Environments
Fritsche, Paul
Kueppers, Simon
Briese, Gunnar
Wagner, Bernardo
[J]. ICINCO: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON INFORMATICS IN CONTROL, AUTOMATION AND ROBOTICS, VOL 2, 2016, : 30 - 36
[9] A Correspondence Framework for ALS Strip Adjustments based on Variants of the ICP Algorithm
Glira, Philipp
Pfeifer, Norbert
Briese, Christian
Ressl, Camillo
[J]. PHOTOGRAMMETRIE FERNERKUNDUNG GEOINFORMATION, 2015, (04): : 275 - 289
[10] Kümmerle J, 2018, IEEE INT C INT ROBOT, P5584, DOI 10.1109/IROS.2018.8593955

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